diff --git a/src/disease_model/models/compartment_model.py b/src/disease_model/models/compartment_model.py
index e58a82f..9ab7b55 100644
--- a/src/disease_model/models/compartment_model.py
+++ b/src/disease_model/models/compartment_model.py
@@ -228,11 +228,10 @@ def predict_with_params(
             population_data, past_health_data, params)
 
         prediction = integrate.solve_ivp(
-            self.differential_equations,
+            fun=lambda t, y: self.differential_equations(t, y, tuple(self.parameter_config.flatten(params))),
             t_span=(0, forecast_length),
             t_eval=np.arange(1, forecast_length + 1),
-            y0=initial_state,
-            args=self.parameter_config.flatten(params))
+            y0=initial_state)
 
         return self.format_output(prediction.y)
 
diff --git a/src/disease_model/models/seir.py b/src/disease_model/models/seir.py
index 61ae770..e7cdb2b 100644
--- a/src/disease_model/models/seir.py
+++ b/src/disease_model/models/seir.py
@@ -75,7 +75,7 @@ def differential_equations(
         # pylint: disable=invalid-name,too-many-locals
         s, e, i, r, _ = compartments
         population = s + e + i + r
-        parameters = self.parameter_config.parse(args)
+        parameters = self.parameter_config.parse(args[0])
         beta = parameters['beta']
         gamma = parameters['gamma']
         sigma = parameters['sigma']
diff --git a/src/economic_model/data/india_input.csv b/src/economic_model/data/india_input.csv
new file mode 100644
index 0000000..c5f4a56
--- /dev/null
+++ b/src/economic_model/data/india_input.csv
@@ -0,0 +1,43 @@
+#NAME?,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+To: (sector in column),"D01T03: Agriculture, forestry and fishing",D05T06: Mining and extraction of energy producing products,D07T08: Mining and quarrying of non-energy producing products,D09: Mining support service activities,"D10T12: Food products, beverages and tobacco","D13T15: Textiles, wearing apparel, leather and related products",D16: Wood and products of wood and cork,D17T18: Paper products and printing,D19: Coke and refined petroleum products,D20T21: Chemicals and pharmaceutical products,D22: Rubber and plastic products,D23: Other non-metallic mineral products,D24: Basic metals,D25: Fabricated metal products,"D26: Computer, electronic and optical products",D27: Electrical equipment,"D28: Machinery and equipment, nec","D29: Motor vehicles, trailers and semi-trailers",D30: Other transport equipment,D31T33: Other manufacturing; repair and installation of machinery and equipment,"D35T39: Electricity, gas, water supply, sewerage, waste and remediation services",D41T43: Construction,D45T47: Wholesale and retail trade; repair of motor vehicles,D49T53: Transportation and storage,D55T56: Accomodation and food services,"D58T60: Publishing, audiovisual and broadcasting activities",D61: Telecommunications,D62T63: IT and other information services,D64T66: Financial and insurance activities,D68: Real estate activities,D69T82: Other business sector services,D84: Public admin. and defence; compulsory social security,D85: Education,D86T88: Human health and social work,"D90T96: Arts, entertainment, recreation and other service activities",D97T98: Private households with employed persons,HFCE: Final consumption expenditure of households,NPISH: Final consumption expenditure of non-profit institutions serving households,GGFC: Final consumption expenditure of general government,GFCF: Gross Fixed Capital Formation,INVNT: Changes in inventories,CONS_ABR: Direct purchases abroad by residents (imports),CONS_NONRES: Direct purchases by non-residents (exports),EXPO: Exports (cross border),IMPO: Imports (cross border)
+"TTL_01T03: Agriculture, forestry and fishing","64,476.7 ",0.9 ,0.4 ,21.0 ,"1,03,501.1 ","12,874.9 ","5,401.9 ","2,852.7 ",81.1 ,"4,403.5 ","1,459.3 ",75.2 ,4.4 ,0.8 ,0.6 ,1.3 ,1.1 ,79.2 ,7.6 ,"1,471.0 ",361.0 ,"3,959.3 ","4,704.8 ",169.9 ,"9,023.4 ",5.5 ,13.5 ,1.2 ,6.1 ,0.2 ,"1,966.0 ","1,879.4 ",0.1 ,344.7 ,716.7 ,0.0 ,"2,36,888.2 ",0.0 ,0.2 ,"2,253.1 ","1,166.0 ",77.4 ,237.4 ,"4,844.3 ","-5,373.2 "
+TTL_05T06: Mining and extraction of energy producing products,7.4,3194.4,55.7,301.8,75.3,120.7,7.8,78.4,95013.2,573.1,17.6,848.9,10813.3,15.3,0.2,4.1,97.9,150,13.7,37.4,23008.2,73.1,262.2,79.5,202.6,3.1,38.7,0.5,3.4,0.2,86.1,1.4,0.5,95.5,189.9,0,434.9,0,2.5,14.1,3.7,3.3,0,189,-87021.3
+TTL_07T08: Mining and quarrying of non-energy producing products,14.8 ,36.8 ,972.8 ,38.4 ,0.4 ,2.8 ,0.3 ,7.1 ,4.3 ,19.2 ,5.7 ,"4,339.2 ","19,476.1 ",354.1 ,20.9 ,157.1 ,111.4 ,103.6 ,25.2 ,309.3 ,74.7 ,"8,452.7 ",98.5 ,0.8 ,10.6 ,0.1 ,4.1 ,0.2 ,0.2 ,0.0 ,46.6 ,0.3 ,0.1 ,1.6 ,40.4 ,0.0 ,5.5 ,0.0 ,0.1 ,1.7 ,-5.3 ,1.9 ,0.0 ,"1,817.8 ","-18,739.8 "
+TTL_09: Mining support service activities,1448,4310.8,788.5,747.6,92.8,0.1,6.2,40,332.9,71.4,98.4,72.3,22.5,2,0.1,0.4,61.7,1.8,0.9,42.4,854.9,3256.4,0.7,55.8,0.3,0,0,0.1,0.5,0,8.2,176.1,64.8,1.5,1.6,0,0.5,0,638.8,0.7,1.9,0.3,0,202.3,-138.1
+"TTL_10T12: Food products, beverages and tobacco","6,491.7 ",45.4 ,25.2 ,4.6 ,"9,159.8 ",460.6 ,13.3 ,69.7 ,377.7 ,"2,175.2 ",402.0 ,100.6 ,174.5 ,41.7 ,26.4 ,59.3 ,53.6 ,117.3 ,34.5 ,88.8 ,162.4 ,307.5 ,"1,193.1 ",32.5 ,"11,382.6 ",20.0 ,30.7 ,15.3 ,22.6 ,14.2 ,449.7 ,"1,136.4 ",22.4 ,445.8 ,867.1 ,0.0 ,"1,49,831.6 ",0.1 ,3.1 ,132.9 ,81.4 ,305.9 ,946.0 ,"17,424.4 ","-18,953.7 "
+"TTL_13T15: Textiles, wearing apparel, leather and related products",600.6,132.4,63.9,20,137.6,38828.2,75.3,260.2,661.5,2657,1296.7,273.5,258.1,104.5,76.5,192.3,193.9,1369.2,197.1,2660.7,174,1760.5,1138.6,267.6,374.5,106.5,57,79.1,341.7,28.1,824.3,728.3,106,383.4,672.9,0,64794.4,0,0.3,451.3,228.2,136.4,658.2,37088.3,-9330.2
+TTL_16: Wood and of products of wood and cork (except furniture),124.9 ,100.2 ,40.3 ,4.3 ,46.7 ,139.8 ,766.3 ,171.7 ,46.8 ,115.2 ,68.1 ,137.2 ,35.3 ,61.3 ,26.7 ,47.2 ,77.7 ,219.7 ,33.9 ,"2,368.1 ",19.9 ,"6,603.7 ",173.9 ,12.3 ,49.2 ,14.4 ,20.0 ,12.9 ,19.1 ,3.2 ,119.2 ,22.7 ,21.1 ,21.6 ,120.8 ,0.0 ,"2,378.9 ",0.0 ,0.2 ,344.6 ,42.1 ,10.0 ,43.1 ,"1,159.6 ",-879.1 
+TTL_17T18: Paper products and printing,270.1,63.2,60.1,6.1,1432.3,980.6,88.1,6313.4,246.3,1302.2,531.9,620.7,153.2,139.5,147.2,275.7,315.6,510.9,86.6,706.3,369.9,1050.5,1720.1,572.8,529.5,2258.1,417.3,1087.2,1803,185.9,1364,1353.7,1328.2,227,677.3,0,1749,0.4,11.8,457.6,46.3,41.1,76.9,1396.8,-5039
+TTL_19: Coke and refined petroleum products,"7,263.2 ","1,768.1 ","1,581.6 ",270.6 ,991.3 ,"2,985.2 ",163.7 ,517.0 ,"29,068.8 ","12,582.8 ","1,752.6 ","2,757.4 ","5,131.1 ",438.2 ,202.3 ,539.3 ,698.5 ,991.4 ,499.4 ,958.5 ,"8,672.0 ","13,649.4 ","4,155.9 ","38,206.5 ",821.1 ,224.2 ,693.5 ,"1,016.2 ","1,730.3 ",685.7 ,"3,606.1 ","3,787.9 ","1,286.3 ",486.2 ,"1,264.5 ",0.0 ,"35,152.9 ",0.0 ,0.4 ,588.9 ,233.1 ,93.2 ,270.4 ,"23,636.9 ","-7,247.4 "
+TTL_20T21: Chemicals and pharmaceutical products,9566.2,1046,527.8,85.6,1208.7,14474,357.4,1738.6,11014.1,46156.3,12530.3,2726.7,3909.4,926.1,719.6,1608.1,1197.4,2945,645.8,2592.3,611.9,8313.6,1542.9,451.5,587.3,195.4,124.7,60,41.1,24.1,1599.2,2202.7,49.3,7495.1,1767.4,0,24414.9,0.1,2.2,1228.8,320.7,72.4,205.6,34364,-49682.9
+TTL_22: Rubber and plastics products,364.9 ,261.5 ,144.3 ,24.6 ,820.7 ,"1,206.6 ",52.7 ,251.2 ,401.8 ,"1,438.2 ","2,744.6 ",422.0 ,429.9 ,273.5 ,371.1 ,773.9 ,900.0 ,"2,929.1 ",454.6 ,"1,079.3 ",134.7 ,"6,659.4 ",959.0 ,"1,442.6 ",204.4 ,55.0 ,222.6 ,146.3 ,14.8 ,66.7 ,513.1 ,284.9 ,132.0 ,135.8 ,212.3 ,0.0 ,"8,985.8 ",0.0 ,0.1 ,"5,073.7 ",829.5 ,2.7 ,7.6 ,"5,687.1 ","-4,331.5 "
+TTL_23: Other non-metallic mineral products,42.4,17.1,15.3,27.5,227.4,330.2,68.4,39.3,238.7,689,291.8,6175.4,785.1,257,299.1,348.3,372.4,928.5,129.8,398.2,199.1,41440.8,423.2,23,175.8,14.2,76.2,17,22.1,11.5,187.2,35.6,26.7,72.2,165,0,486.7,0,0.4,336.7,44.6,1.4,9.5,3120.7,-5142.8
+TTL_24: Manufacture of basic metals,98.7 ,495.4 ,38.2 ,212.6 ,91.2 ,149.0 ,50.2 ,645.7 ,362.9 ,"1,322.5 ",726.7 ,"1,414.2 ","45,078.0 ","16,674.6 ","2,350.7 ","16,461.1 ","18,602.2 ","16,669.2 ","4,958.7 ","11,044.9 ","1,417.5 ","40,469.7 ",616.8 ,60.6 ,45.4 ,21.9 ,305.2 ,63.0 ,48.0 ,41.8 ,146.9 ,166.3 ,94.3 ,93.1 ,266.9 ,0.0 ,967.1 ,0.0 ,0.6 ,"1,991.3 ",187.5 ,2.4 ,50.6 ,"20,524.6 ","-55,898.0 "
+"TTL_25: Fabricated metal products, except machinery and equipment",399.3,468.2,167.2,54.3,501.8,414,95.5,98.1,534.7,539.7,381.8,479.1,3371.5,2811.8,406.7,1270.2,3481.4,3789.5,796.9,1408.9,236.6,15759.7,547.2,162,135.7,36.2,113.1,181.6,33.6,149.1,425.4,620.4,196.7,69.8,176.4,0,1001,0,1.6,3531.8,233.9,3.4,35,6391.5,-6999.7
+"TTL_26: Computer, electronic and optical products",57.6 ,77.2 ,44.6 ,15.7 ,48.2 ,131.4 ,10.0 ,75.4 ,159.7 ,182.4 ,119.5 ,101.0 ,163.2 ,170.9 ,"6,671.2 ","1,365.3 ","1,067.4 ","1,495.1 ",311.3 ,437.8 ,406.0 ,"2,427.4 ",753.7 ,399.7 ,122.7 ,257.6 ,"3,703.2 ","2,927.9 ",900.8 ,55.7 ,664.0 ,"1,219.9 ",468.9 ,351.0 ,489.1 ,0.0 ,"1,952.5 ",0.0 ,0.7 ,"17,592.9 ","2,683.3 ",41.4 ,72.4 ,"2,998.5 ","-28,145.4 "
+TTL_27: Electrical equipment,55.7,72.6,32.2,8.4,54.4,76.3,9.8,33.8,82.1,127.9,92.1,111,669,359.7,716.7,1510.6,876.9,1079.5,238.2,328.2,366.6,3180.8,211,188.5,40.6,32,437.2,298.6,93.4,35.3,157.7,203.3,83.2,41.2,105.8,0,3060.6,0,0.3,30088.5,1589.2,14,57.5,9218.9,-9207.8
+TTL_28: Machinery and equipment n.e.c.,135.8 ,155.0 ,73.4 ,22.7 ,84.1 ,152.6 ,17.6 ,39.9 ,134.5 ,143.5 ,151.2 ,116.9 ,525.8 ,298.3 ,372.3 ,313.5 ,"1,008.7 ","1,036.2 ",249.9 ,250.1 ,149.2 ,"2,307.0 ",188.0 ,189.3 ,30.0 ,22.9 ,207.1 ,179.2 ,73.1 ,27.9 ,157.8 ,265.7 ,65.6 ,33.6 ,65.6 ,0.0 ,"2,242.9 ",0.0 ,1.2 ,"49,080.4 ","2,036.4 ",3.6 ,18.5 ,"14,358.5 ","-17,475.4 "
+"TTL_29: Motor vehicles, trailers and semi-trailers",69,28.3,18.4,7.1,2.2,8.5,0.3,4.7,4.7,34.5,3.9,3.4,39.1,15.6,28.1,25.5,280.3,12730.4,162.3,24.7,71,315.9,433.7,1273.4,15.8,11.7,40.9,43.2,62.7,20.6,303.5,188.8,26,10.9,43.6,0,11792.2,0,0.3,51680.7,4968.3,23.3,82.8,10078.9,-6299.3
+TTL_30: Other transport equipment,49.0 ,1.4 ,0.5 ,0.4 ,1.6 ,2.4 ,0.2 ,1.5 ,3.4 ,5.0 ,1.4 ,1.6 ,10.2 ,5.4 ,4.3 ,6.3 ,75.7 ,24.5 ,"4,608.8 ",4.7 ,3.1 ,32.1 ,46.7 ,515.1 ,2.1 ,1.7 ,5.1 ,8.3 ,3.4 ,5.7 ,118.5 ,741.4 ,4.4 ,2.3 ,17.4 ,0.0 ,"4,484.0 ",0.0 ,0.3 ,"24,535.1 ","2,071.2 ",2.0 ,0.0 ,"7,429.5 ","-14,307.7 "
+TTL_31T33: Other manufacturing; repair and installation of machinery and equipment,101.4,52.1,29.6,8.8,83.5,435.4,89.1,94.7,82.3,209,104.5,122.2,346.3,161.9,252.8,209.2,327,638.7,101.6,2309.6,201.3,2943.3,372.4,247,110.7,73.5,198.3,224.4,362.4,107.5,374.4,556.9,556.3,527.6,290.2,0,7763.3,0,4.7,11146.5,1395.1,114.8,419.3,25324.3,-8034.3
+"TTL_35T39: Electricity, gas, water supply, sewerage, waste and remediation services","2,231.3 ","2,149.6 ","1,703.4 ",130.6 ,"1,465.4 ","3,693.9 ",194.7 ,"1,103.6 ","4,072.3 ","4,512.5 ","1,163.0 ","2,802.6 ","6,932.6 ",658.2 ,328.9 ,540.2 ,"1,064.7 ","1,533.2 ",365.2 ,798.0 ,"26,115.9 ","3,129.3 ","2,981.3 ","3,772.3 ","2,125.1 ",418.9 ,"1,460.2 ","1,139.2 ","2,317.7 ","3,023.8 ","1,996.7 ","4,966.7 ","4,333.6 ","1,080.5 ","1,955.7 ",0.0 ,"37,720.7 ",72.4 ,"1,463.6 ",85.9 ,107.5 ,3.6 ,0.0 ,22.0 ,-663.8 
+TTL_41T43: Construction,479.4,272.3,495.4,36.4,63.6,187.3,20.7,61.9,396.8,286.6,84,237.4,235.4,102.6,50.1,87.9,156.7,182.5,38.7,138,1739.9,32299.4,968.4,1558.6,275.2,87.5,776.3,309.1,1087.2,6096.1,742.1,3281.8,1029.1,228.3,489.4,0,565.6,0,0.7,373205.6,2.3,9.6,0,0,-324.6
+TTL_45T47: Wholesale and retail trade; repair of motor vehicles,"13,154.3 ","1,007.7 ",635.7 ,172.7 ,"20,852.1 ","13,757.9 ","1,048.4 ","1,741.5 ","6,842.9 ","8,857.5 ","2,625.4 ","3,105.6 ","10,422.2 ","2,524.6 ","1,344.4 ","2,585.3 ","3,185.2 ","5,223.0 ","1,649.3 ","3,627.7 ","4,365.2 ","20,110.4 ","21,506.1 ","11,888.2 ","3,817.1 ","1,852.1 ","3,301.6 ","5,068.6 ","2,006.5 ",639.8 ,"7,364.6 ","2,008.1 ",974.9 ,"1,345.6 ","2,005.3 ",0.0 ,"79,080.9 ",4.4 ,64.6 ,"15,067.4 ","1,612.9 ",407.0 ,399.9 ,"23,566.3 ","-34,276.4 "
+TTL_49T53: Transportation and storage,5997.4,1128,930.4,186.2,9140.7,6529.5,492.9,1004.2,5364.8,4162.3,1293.6,3701.9,10334.4,1400.6,627.5,1257.9,1818.1,3417.4,892.8,1931.9,4006.5,16622.9,10724.7,24343,1829.6,721.4,1410.8,941.2,2878.9,324.6,3823.9,3631.2,1511.2,559.3,1433.6,0,75310.8,3.8,2807.6,5541.4,-80.9,1003.7,2564.5,25584.4,-27229
+TTL_55T56: Accomodation and food services,207.2 ,96.9 ,35.8 ,36.1 ,154.1 ,282.5 ,21.8 ,74.2 ,111.4 ,294.8 ,100.6 ,140.5 ,199.5 ,95.3 ,42.8 ,76.4 ,182.3 ,223.4 ,140.8 ,163.3 ,549.0 ,"1,576.1 ","1,443.5 ","1,858.1 ",636.9 ,256.0 ,249.0 ,"1,424.7 ","2,415.9 ",779.3 ,"4,458.1 ","1,986.7 ","1,493.1 ",857.3 ,"1,184.8 ",0.0 ,"24,270.4 ",97.1 ,"2,425.0 ",3.9 ,0.2 ,"3,063.5 ","8,436.8 ",0.0 ,"-3,103.5 "
+"TTL_58T60: Publishing, audiovisual and broadcasting activities",41.2,13.4,9.9,1.3,184,116.6,10.1,547,68.8,180.1,103.6,73,23.1,23.2,36.5,46.6,61.7,124.2,25.3,106.1,83.2,321.1,412.9,169.8,87,1153.2,403.3,558.7,453.3,47.7,370.4,325,401.8,46.9,256.5,0,8603.7,52.7,515.9,4189.3,28.6,52.5,101.3,624.5,-1138.5
+TTL_61: Telecommunications,156.9 ,34.3 ,44.7 ,11.0 ,86.1 ,221.3 ,10.9 ,44.6 ,173.0 ,167.0 ,59.8 ,88.3 ,84.0 ,57.6 ,61.9 ,59.6 ,135.7 ,153.2 ,55.1 ,106.3 ,332.7 ,702.6 ,994.3 ,957.8 ,201.1 ,510.1 ,"11,364.4 ","1,784.3 ","2,513.2 ",262.5 ,"1,024.2 ","1,413.9 ",444.3 ,164.3 ,474.0 ,0.0 ,"34,474.4 ",0.4 ,166.7 ,210.8 ,8.1 ,187.1 ,485.8 ,"2,294.0 ","-1,321.7 "
+TTL_62T63: IT and other information services,4,2,0.6,3.8,4,4.9,0.3,31.3,4.4,11.1,2.8,2.7,6.6,2.6,7.2,4.8,4.7,189.2,55.7,4,366.7,656.5,789.1,758.8,130.7,369.1,1319.5,6887.2,3629.9,216.1,697.3,1278.5,407.8,129.8,377.8,0,5844.9,0.1,1.1,20966.6,261.1,10.1,4.8,78473.1,-4912.3
+TTL_64T66: Financial and insurance activities,"2,588.2 ","1,169.8 ",551.8 ,89.3 ,"1,110.6 ","2,319.4 ",137.5 ,473.3 ,"2,447.4 ","2,081.0 ",658.7 ,984.6 ,"3,148.1 ",572.2 ,427.5 ,609.0 ,"1,161.7 ","1,730.5 ",730.3 ,988.2 ,"3,316.5 ","9,679.4 ","6,332.5 ","6,116.1 ","1,248.7 ",700.4 ,"2,135.5 ","3,280.2 ","30,497.5 ","14,939.7 ","5,195.0 ",466.2 ,"2,012.5 ",676.9 ,"2,103.6 ",0.0 ,"74,562.1 ",0.3 ,65.2 ,144.1 ,66.0 ,169.0 ,357.3 ,"8,667.1 ","-20,683.1 "
+TTL_68: Real estate activities,37,19.8,3.4,0.9,35.9,89.8,4.4,19.8,47.6,43.7,26.7,25.9,21.3,22.4,14.2,19.7,32.5,78.7,20.6,46.9,88.3,450.3,821.9,300,277.9,62.3,241.1,288.2,545.2,376.9,330.6,363.2,308,85.9,208.6,0,166366.5,0,201.8,2185.5,3.9,432.9,1309.4,591.2,-1111
+TTL_69T82: Other business sector services,"1,662.7 ","1,479.9 ",321.1 ,167.9 ,"1,539.2 ","1,208.8 ",138.0 ,543.1 ,"2,204.3 ","3,320.8 ",672.4 ,"1,167.0 ",784.9 ,615.5 ,"1,044.6 ",698.9 ,"1,239.4 ","1,868.4 ",743.6 ,"1,155.8 ","2,321.2 ","15,299.2 ","7,841.9 ","10,597.7 ","2,321.4 ","1,975.1 ","4,314.0 ","8,183.9 ","7,014.7 ","2,912.2 ","15,226.7 ","4,633.1 ","2,843.9 ","1,168.5 ","2,788.7 ",0.0 ,"6,122.1 ",155.0 ,"2,067.7 ","4,718.6 ",154.6 ,227.6 ,537.0 ,"19,471.7 ","-19,718.0 "
+TTL_84: Public administration and defence; compulsory social security,1.6,0.6,1.3,2.9,0.8,1.6,0.5,7.2,1.7,3.1,0.9,3.6,26.6,0.9,0.5,0.8,1,39.1,13.9,1.2,30.3,13.4,140.8,12.5,28.2,10.4,96.9,5.2,11.5,1,111.5,599.9,1.1,42.9,95.6,0,2874.8,0.1,152365.5,27.7,0.4,16.7,0,0,-173.8
+TTL_85: Education,18.5 ,45.2 ,20.0 ,0.9 ,0.4 ,1.0 ,0.1 ,5.5 ,1.0 ,2.1 ,0.6 ,0.4 ,0.8 ,0.2 ,0.3 ,0.3 ,0.4 ,45.7 ,6.6 ,0.4 ,68.2 ,107.8 ,128.7 ,152.5 ,9.1 ,40.6 ,102.2 ,263.1 ,435.7 ,26.9 ,244.8 ,1.8 ,"1,071.6 ",63.8 ,135.5 ,0.0 ,"36,838.5 ",0.0 ,"54,068.5 ",48.3 ,0.4 ,833.9 ,471.7 ,0.0 ,-942.7 
+TTL_86T88: Human health and social work,174,14.5,1.1,0.1,172.7,28.2,11.3,42.7,108.7,499,64.5,70.3,43.8,48.2,32.1,57.7,92.1,149.8,20,128.1,264.3,537.1,626.5,859.4,122.4,121.4,320.1,637.3,1287.6,126.3,785.5,1155.5,118.7,2155.1,880.9,0,31427.9,6390.1,0.3,551.3,0.8,131.8,318.9,0,-179.6
+"TTL_90T96: Arts, entertainment, recreation and other service activities",318.6 ,289.6 ,326.8 ,22.2 ,175.2 ,455.3 ,27.3 ,74.4 ,340.7 ,223.8 ,92.1 ,153.3 ,511.6 ,69.8 ,93.0 ,98.1 ,153.5 ,294.6 ,87.3 ,195.6 ,454.4 ,"1,831.9 ","1,295.7 ","1,357.4 ",826.4 ,518.9 ,699.5 ,"1,483.7 ","1,564.1 ",224.4 ,"1,470.2 ","1,263.9 ",893.3 ,726.9 ,"3,322.4 ",0.0 ,"20,069.8 ","12,096.1 ","7,456.6 ","1,068.9 ",1.1 ,496.3 ,"1,162.5 ",541.9 ,-953.3 
+TTL_97T98: Private households with employed persons,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3498.3,0,0,0,0,0,0,0,0
+TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries),42.9 ,14.7 ,7.0 ,1.7 ,9.2 ,118.1 ,2.4 ,15.1 ,158.3 ,152.3 ,42.1 ,20.9 ,134.3 ,33.6 ,60.4 ,58.2 ,51.1 ,78.7 ,21.3 ,46.7 ,62.6 ,142.8 ,47.0 ,137.7 ,16.0 ,5.7 ,33.4 ,26.8 ,28.5 ,2.4 ,35.6 ,40.8 ,12.2 ,20.8 ,19.1 ,0.0 ,457.3 ,0.2 ,4.8 ,462.4 ,47.3 ,367.6 ,0.0 ,0.0 ,"-3,040.4 "
+"TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)",2250.2,937.3,631,147.4,1077.4,3232.2,88.9,373.5,13060.8,4973.1,825.8,1415,3661.9,732.6,509.6,836.9,1010.5,2230.2,502.5,1219.1,4087.1,10456.5,3185.6,13236.1,2020.7,231.3,653.3,710.4,987.1,421.6,1836.8,1994.4,688.6,638,880.6,0,86917.1,11.6,30.3,19032.8,3158.2,0,912.6,9931.3,0
+TTL_INT_FNL: Total intermediate consumption at purchasers’ prices,"1,21,003.0 ","21,002.5 ","10,399.8 ","2,893.5 ","1,54,632.8 ","1,06,021.4 ","9,484.1 ","19,496.0 ","1,74,256.4 ","1,04,518.6 ","30,596.5 ","34,891.5 ","1,27,966.9 ","30,072.4 ","17,375.0 ","32,207.1 ","40,152.0 ","65,370.6 ","18,925.7 ","39,178.8 ","85,746.1 ","2,76,899.6 ","79,783.2 ","1,22,426.5 ","39,837.7 ","12,388.6 ","35,589.7 ","39,353.7 ","65,256.5 ","31,884.7 ","58,831.8 ","44,982.8 ","23,078.5 ","20,830.7 ","26,762.9 ",0.0 ,"12,53,392.9 ","18,885.1 ","2,24,375.6 ","6,48,041.9 ","23,529.6 ","8,363.8 ","20,253.3 ","3,97,023.6 ","-4,78,018.2 "
+VALU: Value added at basic prices,338956.7,28094.9,7412.5,10374.4,31163.4,45107.2,5480.8,8439.6,29918.9,47448.8,12186.7,18536.3,21163.3,14440.4,7674,14624.3,19358.2,23309.4,11604.5,11860.5,51326.8,151141.7,198761,99455.1,19204.2,7527.9,25871,79657.1,110771.6,143454.9,66922.9,111439.5,71242.5,29567.9,37112.8,3498.3,0,0,0,0,0,0,0,0,0
+OUTPUT: Output at basic prices,"4,59,959.6 ","49,097.4 ","17,812.3 ","13,267.9 ","1,85,796.2 ","1,51,128.7 ","14,964.9 ","27,935.7 ","2,04,175.3 ","1,51,967.3 ","42,783.2 ","53,427.7 ","1,49,130.2 ","44,512.8 ","25,049.0 ","46,831.4 ","59,510.2 ","88,680.0 ","30,530.2 ","51,039.3 ","1,37,072.8 ","4,28,041.3 ","2,78,544.2 ","2,21,881.7 ","59,041.9 ","19,916.5 ","61,460.7 ","1,19,010.9 ","1,76,028.0 ","1,75,339.6 ","1,25,754.7 ","1,56,422.4 ","94,321.0 ","50,398.7 ","63,875.7 ","3,498.3 ",0.0 ,0.0 ,0.0 ,0.0 ,0.0 ,0.0 ,0.0 ,0.0 ,0.0 
diff --git a/src/economic_model/data/sector_mapping.csv b/src/economic_model/data/sector_mapping.csv
index 9a098fa..6430266 100644
--- a/src/economic_model/data/sector_mapping.csv
+++ b/src/economic_model/data/sector_mapping.csv
@@ -1,25 +1,25 @@
-lockdown_sector,sector
-agriculture,
-chemical,Manufacturing
-commerce,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-construction,Construction
-education,Other services
-fin_prof_services,"Financial, Real Estate and Professional Services"
-food_consumables,
-healthcare,Other services
-hospitality_tourism,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-manufacturing,Manufacturing
-mining,Mining and quarrying
-media,Other services
-energy,"Electricity, gas, water supply & other utility services"
-telecom,"Transport, storage, communication & services related to broadcasting"
-public_admin,"Public Administration Defence, and Other Services"
-supply_chain_ship,"Transport, storage, communication & services related to broadcasting"
-forest_husb_fish,
-textiles,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-transportation ,"Transport, storage, communication & services related to broadcasting"
-utilities,"Electricity, gas, water supply & other utility services"
-open_border,"Transport, storage, communication & services related to broadcasting"
-air_travel,"Transport, storage, communication & services related to broadcasting"
-roal_rail_travel,"Transport, storage, communication & services related to broadcasting"
-public_transport,"Transport, storage, communication & services related to broadcasting"
+lockdown_sector,sector,id
+agriculture,"TTL_01T03: Agriculture, forestry and fishing",0
+chemical,TTL_20T21: Chemicals and pharmaceutical products,9
+commerce,TTL_69T82: Other business sector services,30
+construction,TTL_41T43: Construction,21
+education,TTL_85: Education,32
+fin_prof_services,TTL_64T66: Financial and insurance activities,28
+food_consumables,"TTL_10T12: Food products, beverages and tobacco",4
+healthcare,TTL_86T88: Human health and social work,33
+hospitality_tourism,"TTL_90T96: Arts, entertainment, recreation and other service activities",34
+manufacturing,TTL_24: Manufacture of basic metals,12
+mining,TTL_05T06: Mining and extraction of energy producing products,1
+media,"TTL_58T60: Publishing, audiovisual and broadcasting activities",25
+energy,TTL_05T06: Mining and extraction of energy producing products,1
+telecom,TTL_61: Telecommunications,26
+public_admin,TTL_84: Public administration and defence,31
+supply_chain_ship,TTL_49T53: Transportation and storage,23
+forest_husb_fish,"TTL_01T03: Agriculture, forestry and fishing",0
+textiles,"TTL_13T15: Textiles, wearing apparel, leather and related products",5
+transportation ,TTL_49T53: Transportation and storage,23
+utilities,"TTL_35T39: Electricity, gas, water supply, sewerage, waste and remediation services",20
+open_border,TTL_49T53: Transportation and storage,23
+air_travel,TTL_49T53: Transportation and storage,23
+roal_rail_travel,TTL_49T53: Transportation and storage,23
+public_transport,TTL_49T53: Transportation and storage,23
diff --git a/src/economic_model/models/__init__.py b/src/economic_model/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/economic_model/models/basic_lockdown_model.py b/src/economic_model/models/basic_lockdown_model.py
index 10cd9af..abf3fc6 100644
--- a/src/economic_model/models/basic_lockdown_model.py
+++ b/src/economic_model/models/basic_lockdown_model.py
@@ -3,9 +3,12 @@
 """
 from typing import Dict
 import attr
+import os
 import numpy as np
+import pandas as pd
 from help_project.src.economic_model.utils import gva_data
 from help_project.src.exitstrategies import lockdown_policy as lockdown
+from help_project.src.economic_model.models.economy_simulation import simulate_economy
 
 
 class EconomicLockdownModel():
@@ -33,19 +36,32 @@ def _get_economic_vector_for_single_policy(self, lockdown_policy: lockdown.Lockd
                     sector_mappings.iloc[i]['lockdown_sector'])
 
         baseline_gva = gva.get_gvas()
-        adjusted_gva = {}
+        india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
+        india_input_output.columns = india_input_output.iloc[0]
+        india_input_output = india_input_output.iloc[1:]
+        india_input_output = india_input_output.set_index('To: (sector in column)')
+        india_input_output = india_input_output.drop(
+            ['TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries)',
+             'TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)',
+             'TTL_INT_FNL: Total intermediate consumption at purchasers’ prices',
+             'TTL_97T98: Private households with employed persons'], axis=0)
+        india_input_output = india_input_output.drop(columns=india_input_output.columns[-8:])
+        india_input_output = india_input_output.drop(columns=[india_input_output.columns[-2]])
+        sectors = india_input_output.index.values[:-2]
+        labor_shocks = {}
+        for sector in sectors:
+            labor_shocks[sector]=1
         for key in mapping_dict:
-            if key not in baseline_gva:
+            if key not in sectors:
                 continue
             weights = []
             for sector in mapping_dict[key]:
                 if sector in lockdown_policy:
                     weights.append(lockdown_policy[sector])
             if len(weights) > 0:
-                adjusted_gva[key] = baseline_gva[key] * np.mean(weights)
-            else:
-                adjusted_gva[key] = baseline_gva[key]
-        return adjusted_gva
+                labor_shocks[key] = np.mean(weights)
+        adjusted_gvas = simulate_economy(labor_shocks)
+        return adjusted_gvas
 
     def get_economic_vector(
             self, lockdown_policy: lockdown.LockdownTimeSeries) -> Dict[str, float]:
diff --git a/src/economic_model/models/economy_simulation.py b/src/economic_model/models/economy_simulation.py
new file mode 100644
index 0000000..8f35c55
--- /dev/null
+++ b/src/economic_model/models/economy_simulation.py
@@ -0,0 +1,138 @@
+from __future__ import division  # makes division work correctly
+import random
+from builtins import range
+from help_project.src.economic_model.models.firm import Firm
+from help_project.src.economic_model.models.household import Household
+from abcEconomics import Simulation
+import pandas as pd
+import numpy as np
+import sys,os
+""" 
+1. declares the timeline
+2. build one Household agent representing all the households and one Firm each for each sector
+3. For every labor_endowment an agent has gets one trade or usable labor per round. If it is not used at the end of the round it disaapears.
+4. Firms' and Households' possesions are monitored ot the points marked in timeline.
+"""
+
+# Disable
+def blockPrint():
+    sys.stdout = open(os.devnull, 'w')
+
+# Restore
+def enablePrint():
+    sys.stdout = sys.__stdout__
+
+# blockPrint()
+def main(parameters, agent_parameters, labor_alphas, shocks_to_labor_supply):
+    simulation = Simulation(processes=1)
+
+    firms = simulation.build_agents(
+        Firm, 'firm', num_sectors = len(agent_parameters), agent_parameters=agent_parameters)
+    households = simulation.build_agents(
+        Household, 'household', number=1, firms_info=agent_parameters, labor_alphas = labor_alphas[0], needed_labor = labor_alphas[1])
+
+
+    for rnd in range(parameters['rounds']):
+        if rnd>5:
+            # for i in range(number_sectors):
+                # enablePrint()
+                # print(firms[i].last_output)
+                # blockPrint()
+            households.apply_shocks(shocks_to_labor_supply)
+        simulation.advance_round(rnd)
+        households.create_labor()
+        households.sell_labor()
+        firms.buy_labor()
+        firms.production()
+        firms.panel_log(goods=['money', 'GOOD_sector1', 'GOOD_sector2'],
+                        variables=['last_output'])
+        firms.quotes()
+        households.buy_goods()
+        firms.buy_goods()
+        firms.sell_goods()
+        households.panel_log(goods=['money', 'GOOD_sector1', 'GOOD_sector2'],
+                           variables=['current_utility'])
+        households.consumption()
+        firms.adjust_target()
+        # firms.adjust_price()
+    simulation.finalize()
+    firms = pd.read_csv(simulation.path +'/panel_firm.csv')
+    shocks = []
+    for i in range(len(agent_parameters)):
+        temp_firms = firms.loc[firms['name']=='firm'+str(i)]
+        shocks.append(temp_firms['last_output'].iloc[-1]/temp_firms['last_output'].iloc[0])
+    return shocks
+
+def comma_convert(stri):
+    return float(stri.replace(",", ""))
+
+def series_convert(series):
+    return series.apply(comma_convert)
+
+def series_normalize(series):
+    return (series / np.sum(series.iloc[:-1]))
+
+
+def simulate_economy(shocks_to_labor_supply=None):
+    parameters = {'name': '2x2',
+                  'random_seed': None,
+                  'rounds': 500,}
+
+    # Cleaning input output table for india
+    india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
+    india_input_output.columns = india_input_output.iloc[0]
+    india_input_output = india_input_output.iloc[1:]
+    india_input_output = india_input_output.set_index('To: (sector in column)')
+    india_input_output = india_input_output.apply(series_convert)
+    india_input_output.iloc[-2] = india_input_output.iloc[-2] + india_input_output.iloc[-4] + india_input_output.iloc[-5] +india_input_output.iloc[-6]
+    india_input_output = india_input_output.drop(['TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries)' , 'TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)', 'TTL_INT_FNL: Total intermediate consumption at purchasers’ prices', 'TTL_97T98: Private households with employed persons'], axis = 0)
+    india_input_output = india_input_output.drop(columns = india_input_output.columns[-8:])
+    india_input_output = india_input_output.drop(columns = [india_input_output.columns[-2]])
+
+    number_sectors = len(india_input_output)-2
+    ratio_df = india_input_output.iloc[:,:-1].apply(series_normalize)
+    labor_df = india_input_output.iloc[:-2,-1]/np.sum(india_input_output.iloc[:-2,-1])
+
+    agent_parameters = []
+    labor_dict = {}
+    labor_requirement = 0
+    for i in range(number_sectors):
+        temp_dict = {}
+        temp_dict['sector'] = 'sector'+str(i)
+        ratio_dict = {}
+        for j in range(number_sectors):
+            if ratio_df.iloc[j,i] > 0.00001:
+                ratio_dict['GOOD_sector'+str(j)] = ratio_df.iloc[j,i]
+        ratio_dict['labor'] = ratio_df.iloc[number_sectors,i]
+        temp_dict['ratio_dict'] = ratio_dict
+        agent_parameters.append(temp_dict)
+        labor_dict['GOOD_sector'+str(i)] = labor_df.iloc[i]
+
+    output_input_ratio = [0]*number_sectors
+    for i in range(number_sectors):
+        for key in agent_parameters[i]['ratio_dict'].keys():
+            if key=="labor":
+                labor_requirement+=agent_parameters[i]['ratio_dict'][key]
+                continue
+            output_input_ratio[int(key.split('r')[-1])] += agent_parameters[i]['ratio_dict'][key]
+
+    for key in labor_dict.keys():
+        output_input_ratio[int(key.split('r')[-1])] += labor_dict[key]
+    labor_alphas = [labor_dict, labor_requirement]
+    for i in range(number_sectors):
+        agent_parameters[i]['output_input_ratio']=output_input_ratio[i]
+    shocks_array=[]
+    for i in range(len(india_input_output)-2):
+        if india_input_output.index[i] in shocks_to_labor_supply.keys():
+            shocks_array.append(0.5 + 0.5*(shocks_to_labor_supply[india_input_output.index[i]]))
+        else:
+            shocks_array.append(1)
+    shocks_to_labor_supply = shocks_array
+    if shocks_to_labor_supply is None:
+        shocks_to_labor_supply = [random.uniform(0.5,1) for i in range(number_sectors)]
+    shocks =  main(parameters,agent_parameters,labor_alphas, shocks_to_labor_supply)
+    GVAs={}
+    for i in range(number_sectors):
+        GVAs[india_input_output.index[i]]=(shocks[i]*india_input_output.iloc[-1,i])
+    return GVAs
+
diff --git a/src/economic_model/models/firm.py b/src/economic_model/models/firm.py
new file mode 100644
index 0000000..61c9d76
--- /dev/null
+++ b/src/economic_model/models/firm.py
@@ -0,0 +1,183 @@
+from __future__ import division  # makes division work correctly
+import random
+import abcEconomics as abce
+import copy
+import numpy as np
+import sys,os
+
+def blockPrint():
+    sys.stdout = open(os.devnull, 'w')
+
+# Restore
+def enablePrint():
+    sys.stdout = sys.__stdout__
+class Firm(abce.Agent, abce.Firm):
+    def init(self, sector, output_input_ratio, num_sectors, ratio_dict ):
+        """
+        1. Gets an initial amount of money
+        2. creates a production function that produces using fixed ratios, any extra input doesn't get used and is wasted.
+        """
+        self.create('money', 10)
+        self.buy_ratio = 1
+        self.ratio_dict = copy.deepcopy(ratio_dict)
+        self.output_input_ratio = output_input_ratio
+        self.last_output = 0
+        for key in ratio_dict.keys():
+            if key=="labor":
+                continue
+            self.create(key,ratio_dict[key])
+        self.sector = sector
+        self.expenses = 1
+        self.num_sectors = num_sectors
+        self.mygood = "GOOD_%s" % self.sector  # GOOD1 if self.id == 1
+        self.target = 1
+        self.target_input = copy.deepcopy(ratio_dict)
+        self.labor_available = 1
+
+        # Not using cobb douglas, fixed ratios for now
+        # self.production_function = self.create_cobb_douglas(self.mygood, 1, {"labor": 1})
+
+        self.production_function = self.create_production_function(ratio_dict)
+        self.price = 1
+        self.inventory = 0
+
+    def create_production_function(self, ratio_dict):
+        """
+        creates production function
+        :param ratio_dict: uses the ratio for each input good to define the production function
+        :return: returns the production function
+        """
+        def production_function(**goods):
+            """
+            production function for this firm, takes input in fixed ratios, if anything extra is given over and above the ratio it's useless
+            :param goods: dictionary with available input goods
+            :return: final produce with remaining extra input goods
+            """
+            ratios = [goods[name] / ratio_dict[name]
+                      for name in ratio_dict.keys()]
+            output = (self.output_input_ratio*2)*min(ratios)
+            self.last_output = output
+            output_dict = {}
+            for name in ratio_dict.keys():
+                if name == "labor":
+                    output_dict[name] = 0
+                    continue
+                output_dict[name] = goods[name] - (min(ratios)*ratio_dict[name])
+            # enablePrint()
+            # print({self.mygood:output})
+            # blockPrint()
+            # print("\n\n\n\n\n\n\n")
+            output_dict[self.mygood]+= output
+
+            return output_dict
+        return production_function
+
+    def buy_labor(self):
+        """
+        receives all labor offers and accepts them according the need and money
+        :return:
+        """
+        # self.create('money',2)
+        # print(self.id, self.possession('money'))
+        oo = self.get_offers("labor")
+        self.labor_available = 0
+        for offer in oo:
+            self.labor_available += offer.quantity
+            self.accept(offer, min(offer.quantity,self.target_input['labor']*self.buy_ratio))
+
+    def buy_goods(self):
+        money = self.possession("money")
+        ### try for slightly more production
+
+        buying_list = {key:max(0,((2*self.target_input[key])-self.possession(key))) for key in self.target_input.keys()}
+
+        quotes = self.get_messages('quote')
+        buy_ratio = 0
+        for key in self.ratio_dict.keys():
+            if key==self.mygood:
+                continue
+            buy_ratio += self.ratio_dict[key]
+
+        buy_ratio = 1
+        self.buy_ratio = buy_ratio
+        for quote in quotes:
+            price = quote.content[1]
+            if quote.content[0] in buying_list.keys():
+                self.buy(quote.sender,
+                        good=quote.content[0],
+                        quantity=buying_list[quote.content[0]],
+                        price=price)
+        #handle keeping your good for next round
+
+    def production(self):
+        """ uses all labor that is available and produces
+        according to the set cobb_douglas function """
+        self.create('money',100)
+
+        a = self.id
+        availability = []
+        for key in self.ratio_dict.keys():
+            availability.append(self.possession(key)/self.ratio_dict[key])
+        min_availability = min(availability)
+        if min_availability < 1:
+            ### try to find good with minimum availability and available resources after that try to maximize production
+            key_min_availability = list(self.ratio_dict.keys())[np.argmin(availability)]
+            # enablePrint()
+            # print(key_min_availability)
+            # print(min_availability)
+            # blockPrint()
+        # self.target_input[key_min_availability] = self.target_input[key_min_availability] * 1.05
+        for key in self.ratio_dict.keys():
+            if abs((self.target_input[key]/self.ratio_dict[key]) - min_availability)<0.0001 and self.target_input[key]>0:
+                self.target_input[key] = self.target_input[key]
+            else:
+                self.target_input[key] = min_availability*self.ratio_dict[key]
+        ## try to buy more for the bottleneck
+
+        self.produce(self.production_function, list(self.ratio_dict.keys()))
+
+
+    def quotes(self):
+        for i in range(self.num_sectors):
+            if i == self.id:
+                continue
+            self.send_envelope(('firm', i), 'quote', (self.mygood, self.price))
+
+        self.send_envelope(('household', 0), 'quote',
+                           (self.mygood, self.price))
+
+    def sell_goods(self):
+        """ sells goods """
+        ### if available quantity less than total demand + need for own production then sell them in the same ratios
+        # if self.id == 18:
+        #     print(self.id)
+        oo = self.get_offers(self.mygood)
+        total_demand = 0
+        for offer in oo:
+            total_demand += offer.quantity
+        if total_demand > (self.possession(self.mygood)-(self.target_input[self.mygood])):
+            sale_ratio = (self.possession(self.mygood))/(total_demand +(2*self.target_input[self.mygood]))
+        else:
+            sale_ratio = 1
+        for offer in oo:
+            self.accept(offer, sale_ratio*offer.quantity)
+        self.inventory = self.possession(self.mygood)
+
+    def adjust_price(self):
+        self.inventory = self.possession(self.mygood)
+        if self.inventory < 0.1:
+            self.price += (0.1 - self.inventory) * \
+                0.01  # random number [0, 0.1]
+        if self.inventory > 0.1:
+            self.price = max(0.01, (self.price - 0.1) *
+                             0.01)  # random number [0, 0.1]
+
+    def adjust_target(self):
+        self.inventory = self.possession(self.mygood)
+        if self.inventory > 2*(self.target_input[self.mygood]):
+            self.target =1
+        else:
+            self.target = 1
+        # self.target_input ={}
+        for key in self.ratio_dict.keys():
+            self.target_input[key]=self.target*self.target_input[key]
\ No newline at end of file
diff --git a/src/economic_model/models/household.py b/src/economic_model/models/household.py
new file mode 100644
index 0000000..56ec63e
--- /dev/null
+++ b/src/economic_model/models/household.py
@@ -0,0 +1,57 @@
+from __future__ import division  # makes division work correctly
+from builtins import range
+import abcEconomics as abce
+import copy
+
+class Household(abce.Agent, abce.Household):
+    def init(self, firms_info, labor_alphas, needed_labor):
+        """ 1. labor_endowment, which produces, because of w.declare_resource(...)
+        in start.py one unit of labor per month
+        2. Sets the utility function to utility = consumption of good "GOOD"
+        """
+        self.create('adult', 1)
+        self.num_firms = len(firms_info)
+        self.firms_info = copy.deepcopy(firms_info)
+        self.alphas = {key:(labor_alphas[key]/sum(labor_alphas.values())) for key in labor_alphas.keys()}
+        cd = self.alphas
+        # creates {GOOD1: 1/3, GOOD2: 1/3, GOOD3: 1/3}
+        self.consumption_function = self.create_cobb_douglas_utility_function(cd)
+        self.current_utility = 0
+        self.needed_labor = needed_labor
+        self.shocks = [1]*self.num_firms
+
+    def create_labor(self):
+        self._inventory.haves['labor']=self.needed_labor
+
+    def sell_labor(self):
+        """ offers one unit of labor to firm 0, for the price of 1 "money" """
+        sum_labor = 0
+        for i in range(self.num_firms):
+            sum_labor += self.firms_info[i]['ratio_dict']['labor']
+
+        for i in range(self.num_firms):
+            self.sell(('firm', i),
+                      good="labor",
+                      quantity=self.possession('labor')*((self.firms_info[i]['ratio_dict']['labor']*self.shocks[i]) / sum_labor),
+                      price=1)
+
+    def buy_goods(self):
+        """ receives the offers and accepts them one by one """
+        money = self.possession("money")
+        self.create('money',20)
+        quotes = self.get_messages('quote')
+        for quote in quotes:
+            price = quote.content[1]
+            self.buy(quote.sender,
+                     good=quote.content[0],
+                     quantity=self.alphas[quote.content[0]],
+                     price=price)
+
+    def consumption(self):
+        """ consumes_everything and logs the aggregate utility. current_utiliy
+        """
+        self.current_utility = self.consume(self.consumption_function, ["GOOD_sector%i" % (i) for i in range(self.num_firms)])
+        # self.log('HH', self.current_utiliy)
+
+    def apply_shocks(self, shocks):
+        self.shocks = copy.deepcopy(shocks)
\ No newline at end of file
diff --git a/src/main.ipynb b/src/main.ipynb
index 12d2f13..e579605 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -12,7 +12,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) solvers for sklearn enabled: https://intelpython.github.io/daal4py/sklearn.html\n"
+     ]
+    }
+   ],
    "source": [
     "# Imports\n",
     "import sys\n",
@@ -151,23 +159,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fit\n",
-      "fit\n"
-     ]
-    }
-   ],
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "# Create and fit a health model\n",
     "seir_model = seir.SEIR()\n",
-    "sir_model = sir.SIR()\n",
+    "# sir_model = sir.SIR()\n",
     "\n",
-    "health_model = ensemble_model.EnsembleModel([sir_model, seir_model])\n",
+    "health_model = ensemble_model.EnsembleModel([seir_model])\n",
     "health_model.fit(population_data, past_health_data, policy_timeseries_by_country['1_ind'][:start])\n",
     "\n",
     "# pm = parameter_mapper.ParameterMapper()\n",
@@ -197,13 +198,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Deaths: 3981827, GDP: 174115.87 - Policy \"1_ind\" applied from 2020-02-29 to 2020-03-25\n",
-      "Deaths: 326366, GDP: 104147.86 - Policy \"1_ind\" applied from 2020-05-04 to 2020-05-18\n"
+      "Deaths: 1455058, GDP: 174115.87 - Policy \"1_ind\" applied from 2020-02-29 to 2020-03-25\n",
+      "Deaths: 310939, GDP: 104147.86 - Policy \"1_ind\" applied from 2020-05-04 to 2020-05-18\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x288 with 1 Axes>"
       ]
@@ -297,7 +298,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -309,7 +310,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -321,7 +322,7 @@
     },
     {
      "data": {
-      "image/png": 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38ahRo+wzzzzjc/mK0KNHD5uWlmavuuoqd9qHH35ok5OTbXh4eIX2VeCqq66ymZmZ7uO0tDTbo0cPv/RVYMaMGfapp54qU920tLQir0VQUJDNycm5ID03t+zfE96sWbPG3ebUqVPt1KlTrbXW7tixw0ZGRtqcnBz77bff2tatW9u8vDx76NAhm5ycbK219vjx4zY0NNTu2LHDWmvtlClT7MyZM6211s6cOdPd1vmGDh1qFy1aZK21duLEifall16y1lq7YcMGe+rUKWuttS+99JIdNmyY1/pFlXvnnXfsLbfcYnNzc+3JkydtbGyszc7OvqB+jx497JYtWy7a6+R5TitWrLB9+vQp1ZiL+77z5PPvMRERqVg7VvwcDya97k4GkmwRMbPfZ8yNMZcB/YF/lVTUS5otQ7rPrLWvWGvjrLVxTZr49hGDV18ugl2rvOftWgWpi8vetocbbriBvXv3Aq7ZxoIZxeeee+6Csp6zuPn5+fzxj38kIiKCyMhI/va3v7F+/XoGDhzoLr927VoGDRp0QTsNGzYkICAAz+vTvXt3GjZs6NOYx44dy5IlSwDX7PeMGTOIiYkhIiKCr7/+GoCjR4/Su3dvoqOjmThxYsEfTQA0adKEgIAAr/2dPHmSm2++2d3eihUr3Oferl07EhISiIyMZMiQIe5PGkJCQpg2bRqdO3emc+fO7utZ1JiTk5Pp0aMHsbGx9OnTh8OHD7vTO3bsSNeuXXnxxRe9nnv//v05deoUXbp04a233mLs2LE8+OCD9OzZk2nTpnHs2DHuuOMOIiMjufbaa91PZ33kkUdISEigd+/ehISEsHTpUqZOnUpERAR9+/YlNzf3gr569+5NYKDrsQTXXnstGRkZAKxYsYIRI0ZQo0YNWrVqRdu2bfniiy9o1qwZMTGu2ziCgoIICwvj4MGD7joJCa5bORISEli+fPkF/Vlr2bBhg/tTGc9yPXv2pHbt2heM5XxFldu5cyc9evQgMDCQOnXq0LFjR95///1CdZcsWUJSUhLx8fFERUVx5swZ1q9fT3R0NBEREdx1112cPXu2Sl+nevXqucudOnUK5xYZn8csIiJVXKGHC1WdpSy3Aluttd877793lqHgvP7gpGcAnk/nCQYOlZAe7CW9uD78I2VB8flbS8j3QV5eHu+99x4REREkJyczb948Pv/8cz777DNeffVVUlJSiqz7yiuvkJaWRkpKCqmpqcTHx3PTTTexa9cuMjNd3zTz5s1j3LhxF9RdunQpLVq0YMuWLeU+B4DGjRuzdetW7r33XvfSkb/85S9069aNlJQU+vfvz/79+93lt2zZQosWLVi6dOkFbdWsWZNly5axdetWPvjgA/7whz+4g/rdu3czYcIEUlNTqVevHi+99JK7Xr169fjiiy9ITEzk/vvvL3Ksubm5/O53v2PJkiUkJydz11138dBDDwEwbtw4Zs+ezebNm4usv3LlSmrVqsW2bdsYPnw4AN988w3r1q3jmWeeYcaMGURHR5OamsoTTzzBnXfe6a67b98+Vq9ezYoVKxg9ejQ9e/bkq6++olatWqxevbrYa/z3v/+dW2+9FYCDBw/SosXPPz7BwcHuwLJAeno6KSkpdOnSBYDvv/+eZs2aAdCsWTN++OHCH5+jR4/SoEEDd8DorV2AuXPnusdSHM9yHTt25L333uP06dMcOXKEDz74gAMHDhQqP2TIEOLi4li4cCHbtm3DGMPYsWN56623+Oqrr8jLy+Pll18uts+qcJ1efPFF2rRpw9SpU5k9e3aJ18lzzOBaJhMdHU2PHj34+OOPS6wvIiK/oCq6xnwksMjj/UqgYGeVBGCFR/qdzu4s1wLZ1trDwBqgtzHmcuemz97AGifvhDHmWmc3ljvPa8tbH/6RfWFAUji/7DNcZ86cISoqiri4OFq2bMn48ePZtGkTAwcOpE6dOtStW5dBgwYV+5/yunXrmDRpkjs4aNiwIcYYxowZw5tvvklWVhabN2/2KYAqr4JZ+djYWNLT0wH46KOPGD16NAC33XYbl1/u2yY61lr+9Kc/ERkZyS233MLBgwf5/nvX338tWrTg+uuvB2D06NFs2rTJXW/kyJHu1+IC6927d7N9+3Z69epFVFQUjz32GBkZGWRnZ5OVlUWPHj0AGDNmjM/nP3ToUAICAgDYtGmTu+5NN93E0aNHyc7OBuDWW2+levXqREREkJ+fT9++rvuaIyIi3NfNm8cff5zAwEDi4+Pd1+h8njOzJ0+eZPDgwTz33HOFZnBLUlK7AG+++SZJSUklrq8/v1zv3r357W9/y3XXXcfIkSPp2rWr+3u3KLt376ZVq1ZcffXVgGtm+qOPPiqyfFW5TpMnT2bfvn3MmjWLxx57rNi2zh9zs2bN2L9/PykpKTz77LOMGjWK48eP+zw2ERHxs9Oegblva8yL/9+unIwxtYFewESP5CeBfxpjxgP7gaFO+rvAb4G9uHZwGQdgrT1mjHkUKJiy/R9r7THn+F7gdaAW8J7zVVwf/lH/SjheTPBdP7jovBIUzLh68vaffXGstV4/Jh83bhz9+vWjZs2aDB06tMTgpyLUqFEDcN3UmpeX504v6mP84ixcuJDMzEySk5OpXr06ISEh5OTkeG3P831Rx+ez1hIeHn5B8J6VlVWm8QLUqVOnUPvnK2i34DpVq1aN6tWru9OrVatW6Lp5mj9/Pu+88w7r1693lw8ODi4025yRkUHz5s0B1ycCgwcPJj4+vtAypiuuuILDhw/TrFkzDh8+TNOmTQHo06cP33//PXFxcbz66qtkZWWRl5dHYGBgoXbB9cfg448/zocffug+l4ceesg921/wPe2tXEHZgk8nRo0aRWhoaLHXtTQ/E1XpOhUYMWIE9957L+D6uUxJSaF58+a8++67RY65Ro0a7msWGxtLmzZt+Oabb4iLi/P5WoiIiB9VtaUs1trT1tpG1tpsj7Sj1tqbrbWhzusxJ91aaydba9tYayOstUkedf5urW3rfM3zSE+y1nZw6iQ6C+qL7MNvokuYMY3xfUbVF927d2f58uWcPn2aU6dOsWzZMm644YYiy/fu3Zs5c+a4A7pjx1yXo3nz5jRv3pzHHnuMsWPHVugYS6N79+4sXLgQgPfee6/I3S3Ol52dTdOmTalevToffPBBoV099u/f7w6oFy1aRLdu3dx5Bbt8vPXWW3Tt2rXI9q+55hoyMzPd7eTm5rJjxw4aNGhA/fr13bPwBWMvLc/z3rhxI40bNy7VbKyn999/n1mzZrFy5Ur3um1wrXNfvHgxZ8+eJS0tjT179tC5c2estYwfP56wsDAefPDBQm3179+f+fNdmxrNnz+fAQMGALBmzRq2bdvGa6+9hjGGnj17utfie5ZLSUlh4sSJrFy50h2sgmvGd9u2be6gvKhy+fn5HD16FIDU1FRSU1Pp3bv3BeccFBTEiRMnAGjXrh3p6enuewYWLFjg/kSjql6nPXv2uNtavXq1+4+PefPmsW3bNndQXtSYMzMzyc937fr07bffsmfPHlq3bn3BOYuISCUpWMoSWBMuq+tTFf9PkV4KokbBnjXebwAN6wcdR1ZodzExMYwdO5bOnTsDcPfddxMdHV1k+bvvvptvvvmGyMhIqlevzj333ENiYiIA8fHxZGZm0r59e5/7HzlyJBs3buTIkSMEBwfzl7/8hfHjx5f5fGbMmMHIkSOJiYmhR48etGzZ0qd68fHx9OvXj7i4OKKiomjXrp07LywsjPnz5zNx4kRCQ0Pds5EAZ8+epUuXLpw7d45FixZ5axqAyy67jCVLlnDfffeRnZ1NXl4e999/P+Hh4cybN4+77rqL2rVr06dPnzKd9yOPPMK4ceOIjIykdu3a7iCvLBITEzl79iy9evUCXDcJzpkzh/DwcIYNG0b79u0JDAzkxRdfJCAggE2bNrFgwQL3dn0ATzzxBL/97W+ZPn06w4YNY+7cubRs2ZJ//cv7fduzZs1ixIgRPPzww0RHR7u/B6ZMmcLJkycZOtT1QVXLli1ZuXLlBfWLKpebm+v+Q7NevXq8+eabXj/NGTt2LJMmTaJWrVps3ryZefPmMXToUPLy8ujUqZPX7QOr0nV64YUXWLduHdWrV+fyyy8v8t+/qDF/9NFH/PnPfyYwMJCAgADmzJnj803ZIiLyC3A/9bMJ+PhJuyntsohfq7i4OJuUlFQobdeuXYSFhfnWQH6ea/eVrQtca8rrB7tmyjuOhGoBfhhxxUhMTCwULPwapKenc/vtt7N9+/YL8kJCQkhKSqJxY98ejStysSvV7zEREak4/9vGtc68WRRM/NCdbIxJttZ6XXeoGfOKEhAI0aNdXxeJ2NhY6tSpwzPPPFPZQxERERH59TiXD6ddyzJ9XV8OCswvacnJyRXW1uTJk/nkk08Kpf3+97/3ugWjv4WEhHidLQeK3dFEREREpEKcPob78ToKzOWXVtRDdkREREQuOYV2ZPF9+ewvsY+5iIiIiMilo9Ae5grMRUREREQqRxn2MAcF5iIiIiIiFeuU54y5AnMRERERkcqhNeaVK+9cHsv2LGPMu2PotaQXY94dw7I9y8g/l1+udgMCAoiKiqJDhw4MHTqU06dPF1u+bl3Xk6UOHTrEkCFDytU3wI033kh6ejohISHutLvuuoumTZvSoUOHEuv/+c9/Zt26daXqMyQkhCNHjriP09PTufHGG0vVRmk98sgjPP3000Dpx5yenl7ktZgyZQrh4eFMmTKlQsZZlClTptCuXTsiIyMZOHAgWVlZ7ryZM2fStm1brrnmGtasWQPAgQMH6NmzJ2FhYYSHh/P888+7yx87doxevXoRGhpKr169inwSa1paGl26dCE0NJThw4fz008/AfD666/TpEkToqKiiIqK4rXXXvNa/+zZswwfPpy2bdvSpUuXC3bM2b9/P3Xr1nX/u5zviSee8Pn6AJw+fZrbbruNdu3aER4ezvTp00scy9q1a4mNjSUiIoLY2Fg2bNjgrpOcnExERARt27blvvvuo6hnQrz//vtcc801tG3blieffNKdPnbsWFq1auW+TgVPRD3fCy+8QNu2bTHGuH8uwPXk2379+tGxY0f3Q69ERKSK8Jwxr12KZ6dYa/VlLbGxsfZ8O3fuvCDNm9z8XHv/hvtth9c7XPB1/4b7bW5+rk/teFOnTh338ahRo+wzzzzjc/mK0KNHD5uWlmavuuoqd9qHH35ok5OTbXh4eIX2VeCqq66ymZmZ7uO0tDTbo0cPv/RVYMaMGfapp54qU920tLQir0VQUJDNycm5ID03t+zfE96sWbPG3ebUqVPt1KlTrbXW7tixw0ZGRtqcnBz77bff2tatW9u8vDx76NAhm5ycbK219vjx4zY0NNTu2LHDWmvtlClT7MyZM6211s6cOdPd1vmGDh1qFy1aZK21duLEifall16y1lo7b948O3ny5BLH/OKLL9qJEydaa61dtGiRHTZsWKH8QYMG2SFDhhT571La7/VTp07ZDRs2WGutPXv2rO3WrZt99913ix3L1q1b7cGDB6211n711Ve2efPm7vY6depkP/30U3vu3Dnbt29fd1ue8vLybOvWre2+ffvs2bNnbWRkpPs6JyQk2H/9618ljnvr1q3un8GCnwtrrX388cfd/zY//PCDvfzyy+3Zs2cvqO/r7zEREalAi0ZZO6Oe6+un04WygCRbRDyqGfMKsGrfKtbt9z7Dum7/OlbtW1Uh/dxwww3s3bsXgGeffZYOHTrQoUMHnnvuuQvKes7i5ufn88c//pGIiAgiIyP529/+xvr16xk4cKC7/Nq1axk0aNAF7TRs2JCAgACaNPl5fVT37t19fvT32LFjWbJkCeCa/Z4xYwYxMTFERETw9ddfA3D06FF69+5NdHQ0EydOLDTz2KRJEwICArz2d/LkSW6++WZ3eytWrHCfe7t27UhISCAyMpIhQ4a4P2kICQlh2rRpdO7cmc6dO7uvZ1FjTk5OpkePHsTGxtKnTx8OHz7sTu/YsSNdu3YtcqvI/v37c+rUKbp06cJbb73F2LFjefDBB+nZsyfTpk3j2LFj3HHHHURGRnLttdeSmpoKuGbvExIS6N27NyEhISxdupSpU6cSERFB3xBv544AACAASURBVL59yc3NvaCv3r17ux9bf+2115KRkQHAihUrGDFiBDVq1KBVq1a0bduWL774gmbNmhETEwNAUFAQYWFhHDx40F0nISEBgISEBJYvX35Bf9ZaNmzY4P5UpqhyxfHsZ8iQIaxfv979b798+XJat25NeHi417rTp0/nzJkzREVFER8fD5T8M1G7dm169uwJwGWXXUZMTEyh6+RtLNHR0TRv3hyA8PBwcnJyOHv2LIcPH+b48eN07doVYwx33nmn1/P/4osvaNu2La1bt+ayyy5jxIgR7u9TX0VHRxf6xKqAMYYTJ05greXkyZM0bNjQ/T0gIiKVrGDG/LIgqF7L52oKzCvA0j1Li81ftndZufvIy8vjvffeIyIiguTkZObNm8fnn3/OZ599xquvvkpKSkqRdV955RXS0tJISUkhNTWV+Ph4brrpJnbt2kVmpmsN1Lx587w+DGjp0qW0aNGCLVu2lPscABo3bszWrVu599573UsU/vKXv9CtWzdSUlLo378/+/fvd5ffsmULLVq0YOnSC69xzZo1WbZsGVu3buWDDz7gD3/4gzuw2717NxMmTCA1NZV69erx0ksvuevVq1ePL774gsTERO6///4ix5qbm8vvfvc7lixZQnJyMnfddRcPPfQQAOPGjWP27Nls3ry5yPorV66kVq1abNu2jeHDhwPwzTffsG7dOp555hlmzJhBdHQ0qampPPHEE9x5553uuvv27WP16tWsWLGC0aNH07NnT7766itq1arF6tWri73Gf//737n11lsBOHjwIC1atHDnBQcHuwPwAunp6aSkpNClSxcAvv/+e5o1awZAs2bN+OGHHy7o4+jRozRo0MAdCJ7f7ttvv+3+o+jAgQNex+k5tsDAQOrXr8/Ro0c5deoUs2bNYsaMGUWe45NPPum+tgsXLiz1z0RWVharVq3i5ptvLnYsnt5++22io6OpUaMGBw8eJDg42J3n7bqe3663cg899BCRkZE88MADnD17tsjxepOYmMiuXbto3rw5ERERPP/881Srpl/pIiJVQsEa81KsLwcF5hXiu9PfFZt/+NThMrddMCsYFxdHy5YtGT9+PJs2bWLgwIHUqVOHunXrMmjQID7++OMi21i3bh2TJk1yB1ENGzbEGMOYMWN48803ycrKYvPmze5gzp8KZuVjY2Pd63g/+ugjRo8eDcBtt93G5Zdf7lNb1lr+9Kc/ERkZyS233MLBgwf5/vvvAWjRogXXX389AKNHj2bTpk3ueiNHjnS/FhdY7969m+3bt9OrVy+ioqJ47LHHyMjIIDs7m6ysLHr06AHAmDFjfD7/oUOHEhAQAMCmTZvcdW+66SaOHj1KdnY2ALfeeivVq1cnIiKC/Px8+vbtC0BERESxTy99/PHHCQwMdM8ie376UMAY4z4+efIkgwcP5rnnnqNevXo+n0dx7fbr14/09HRSU1O55ZZb3DPRvrYxY8YMHnjgAff9Er4ozc9EXl4eI0eO5L777qN169Ylng/Ajh07mDZtGv/3f//nU/kCxZWbOXMmX3/9NVu2bOHYsWPMmjXLhzP92Zo1a4iKiuLQoUNs27aNxMREjh8/Xqo2RETETwr2MS9lYK7PPSvAb2r/hu9OFR2cN6vTrMxtF8wKevL2n31xrLVeg4Zx48bRr18/atasydChQ3+Rj8Fr1KgBuG5qzcvLc6d7G19JFi5cSGZmJsnJyVSvXp2QkBBycnK8tuf5vqjj81lrCQ8PvyB4z8rKKtN4AerUqVOo/fMVtFtwnapVq0b16tXd6dWqVSt03TzNnz+fd955h/Xr17vLBwcHF5qxzsjIcC/NyM3NZfDgwcTHxxdaxnTFFVdw+PBhmjVrxuHDh2natCkAffr04fvvvycuLo5XX32VrKws8vLyCAwMLNRuo0aN3G3dc889TJs2DXDNDhfM9m/bts09tuDgYPLy8sjOzqZhw4Z8/vnnLFmyhKlTp5KVlUW1atWoWbMmiYmJRV7X0vxMTJgwgdDQ0EKflhQ1loJrNnDgQN544w3atGnjLl+wDMbzuh44cIB+/foBMGnSJDp27Fjk9S/4VKJGjRqMGzfO/QmS53Uu6sZZcH3KNX36dIwxtG3bllatWvH111/TuXNnn6+FiIj4Qd5PkOOaaCvNVomgGfMKMSj0wrXZnga2HVhsfml1796d5cuXc/r0aU6dOsWyZcu44YYbiizfu3dv5syZ4w7ojh07BkDz5s1p3rw5jz32GGPHjq3QMZZG9+7dWbhwIQDvvfdekbuAnC87O5umTZtSvXp1PvjgA/7zn/+48/bv3+8OqBctWkS3bt3ceW+99Zb7tWvXrkW2f80115CZmeluJzc3lx07dtCgQQPq16/vnoUvGHtpeZ73xo0bady4calmrT29//77zJo1i5UrV1K7dm13ev/+/Vm8eDFnz54lLS2NPXv20LlzZ6y1jB8/nrCwMB588MFCbfXv35/58+cDrmB/wIABgGuGdtu2bbz22msYY+jZs6d7Lb5nuYJ1+OBazhMWFga4ZvO3bdvm/kPTs58lS5Zw0003YYzh448/Jj09nfT0dO6//37+9Kc/eQ3Kq1ev7l5v7+vPxMMPP0x2dvYFa9CLGktWVha33XYbM2fOdH8CA66gOigoiM8++wxrLW+88QYDBgygRYsW7nOcNGkSnTp1Ys+ePaSlpfHTTz+xePFi+vfvX+g6WWtZvny5+54Qz+tcnJYtW7J+/XrAtfxo9+7d7k8ARESkEpXxqZ+gwLxC9G/Tn1ta3uI175aWt9C/Tf8K7S8mJoaxY8fSuXNnunTpwt133010dHSR5e+++25atmxJZGQkHTt25B//+Ic7Lz4+nhYtWtC+fXuf+x85ciRdu3Zl9+7dBAcHM3fu3HKdz4wZM/joo4+IiYnh3//+Ny1btvSpXnx8PElJScTFxbFw4ULatWvnzgsLC2P+/PlERkZy7Ngx7r33Xnfe2bNn6dKlC88//zx//etfi2z/sssuY8mSJUybNo2OHTsSFRXFp59+CrhmKydPnkzXrl2pVcv3mzo8PfLIIyQlJREZGcn06dPdgWFZJCYmcuLECfeym0mTJgGuGxaHDRtG+/bt6du3Ly+++CIBAQF88sknLFiwgA0bNri363v33XcB142Va9euJTQ0lLVr1xbaVtDTrFmzePbZZ2nbti1Hjx5l/PjxAMyePZvw8HA6duzI7Nmzef31173WHz9+PEePHqVt27Y8++yzhbYS9MWECROIjIwkPj7ep5+JjIwMHn/8cXbu3ElMTEyhrRyLGssLL7zA3r17efTRR93XqWDN/csvv8zdd99N27ZtadOmjdelYIGBgbzwwgv06dOHsLAwhg0b5r6hNT4+noiICCIiIjhy5AgPP/yw1/OcPXu2e4Y+MjKSu+++G4D//u//5tNPPyUiIoKbb76ZWbNm0bhx6f4DEBERPyjjUz8BTGmXRfxaxcXF2aSkpEJpu3btcs/2lSTvXB6r9q1i2d5lHD51mGZ1mjGw7UD6t+lPQLUAfwy5QiQmJhIdHe0Oqn4N0tPTuf3229m+ffsFeSEhISQlJSmAkUtGaX6PiYhIBdi7Ht50VlP0fRKuvbdQtjEm2Vob562q1phXkMBqgQwMHcjA0IpdtuJPsbGx1KlTh2eeeaayhyIiIiLy61DWhwuhwPySlpycXGFtTZ48mU8++aRQ2u9//3uvWzD6W0hIiNfZcqDYHU1EREREyq3QUhYF5lIJinrIjoiIiMglpRxrzHXzp4iIiIhIRSm0K4sCcxERERGRylFojXnDUlVVYC4iIiIiUl75ebB1AaQ7T56uFghfLoZz+T43ocBcRERERKQ88vNgyVhYmQg/nXKlnctzvf9XgivfBwrMK4jNyyPr7bdJHzmKPT1vIn3kKLLefhub7/tfSVXNxo0buf3224vNL3jgTmlkZWXx0ksv+dyPiIiISJX25SLYtcp73q5VkLrYp2YUmFcAm5fHwQce5PBDD3MmJYW8w4c5k5LC4Yce5uD9D2DzfPsrqSzy/Nh2SYoLzIsb1/mBuYiIiMhFLWVB8flbS8h3aLvECpC9YgUn1q71mndi7VqyV6ykweBBZWr70UcfZeHChbRo0YLGjRsTGxvLO++8w3XXXccnn3xC//79GTJkCHfddReZmZk0adKEefPm0bJlS8aOHcvtt9/OkCFDAKhbty4nT55k48aNPPLIIzRu3Jjt27cTGxvLm2++iTGG999/n/vvv5/GjRsTExNT5LjS09OZM2cOAQEBvPnmm/ztb39j7ty5NGzYkJSUFGJiYggKCqJu3br88Y9/BKBDhw688847TJ8+nX379hEVFUWvXr247bbbOHnyJEOGDLlgPCIiIiJVXvbBEvIzfGpGM+YVIGvJ28Xnv118flGSkpJ4++23SUlJYenSpSQlJf3cZlYWH374IX/4wx9ITEzkzjvvJDU1lfj4eO67774S205JSeG5555j586dfPvtt3zyySfk5ORwzz33sGrVKj7++GO+++67IuuHhIQwadIkHnjgAbZt28YNN9wAwDfffMO6deuKfZrok08+SZs2bdi2bRtPPfVUkeMRERERuSjUv7KE/GCfmvFrYG6MaWCMWWKM+doYs8sY09UY09AYs9YYs8d5vdwpa4wxs40xe40xqcaYGI92Epzye4wxCR7pscaYr5w6s40zxVpUH/6SW0wAC5B7+HCZ2t20aRMDBgygVq1aBAUF0a9fP3fe8OHD3cebN29m1KhRAIwZM4ZNmzaV2Hbnzp0JDg6mWrVqREVFkZ6eztdff02rVq0IDQ3FGMPo0aNLPeahQ4cSEBBQ6nrexiMiIiJyUYgeU3x+TAn5Dn/PmD8PvG+tbQd0BHYB04H11tpQYL3zHuBWINT5mgC8DK4gG5gBdAE6AzM8Au2XnbIF9fo66UX14RfVf/Ob4vObNStTu9baIvPq1KlTZF7BEpDAwEDOnTvnbuunn35yl6lRo4b7OCAgwL0mvLzLRzzH5dk/QE5OTpH1ihqPiIiISJUXNQrC+nnPC+sHHUf61IzfAnNjTD2gOzAXwFr7k7U2CxgAzHeKzQfucI4HAG9Yl8+ABsaYZkAfYK219pi19kdgLdDXyatnrd1sXRHsG+e15a0Pv2gwZHDx+YOLzy9Kt27dWLVqFTk5OZw8eZLVq1d7LXfdddexeLHrbt+FCxfSrVs3wLXcJDk5GYAVK1aQm5tbbH/t2rUjLS2Nffv2AbBo0aJiywcFBXHixIki80NCQti6dSsAW7duJS0tzad6IiIiIheVagEw5HVofePPab/pCANehKHzXfm+NOOPsTlaA5nAPGNMijHmNWNMHeAKa+1hAOe1qVP+SuCAR/0MJ6249Awv6RTTRyHGmAnGmCRjTFJmZmaZT7T+HXcQ1KuX17ygXr2of8eAMrXbqVMn+vfvT8eOHRk0aBBxcXHUr1//gnKzZ89m3rx5REZGsmDBAp5//nkA7rnnHj788EM6d+7M559/XuwsO0DNmjV55ZVXuO222+jWrRtXXXVVseX79evHsmXLiIqK4uOPP74gf/DgwRw7doyoqChefvllrr76agAaNWrE9ddfT4cOHZgyZYqvl0NERESk6goIhBr1fn4/7l2IHu1zUA5gilsuUR7GmDjgM+B6a+3nxpjngePA76y1DTzK/WitvdwYsxqYaa3d5KSvB6YCNwE1rLWPOen/DZwGPnLK3+Kk3wBMtdb2M8ZkeeujuPHGxcVZz5srAXbt2kVYWJhP52vz8shesZKst98m9/BhqjdrRoPBg6l/xwBMGdZcFzh58iR169bl9OnTdO/enVdeeaXY3VJERDyV5veYiIiU02u3QMYWV4D+Xwe8FjHGJFtr47zl+XO7xAwgw1r7ufN+Ca613t8bY5pZaw87y1F+8CjfwqN+MHDISb/xvPSNTnqwl/IU04ffmMBAGgweVOZtEYsyYcIEdu7cSU5ODgkJCQrKRURERKqq486GH0Flu7/Qb4G5tfY7Y8wBY8w11trdwM3ATucrAXjSeV3hVFkJJBpjFuO60TPbCazXAE943PDZG/gva+0xY8wJY8y1wOfAncDfPNry1sdF5x//+EdlD4F58+a5l8cUuP7663nxxRcraUQiIiIiVcy5c3DS2amvXhULzB2/AxYaYy4DvgXG4VrX/k9jzHhgPzDUKfsu8FtgL66lKuMAnAD8UWCLU+5/rLXHnON7gdeBWsB7zhe4AnJvfZSatfaSf9DNuHHjGDduXGUPQ0RKyV9LFUVExIvTR+Ccs6tcUPMyNeHXwNxauw3wtobmZi9lLTC5iHb+DvzdS3oS0MFL+lFvfZRWzZo1OXr0KI0aNbrkg3MRubhYazl69Cg1a9as7KGIiFwajh/6+Tio+K20i+LvGfOLWnBwMBkZGZRnxxYRkcpSs2ZNgoN9e9qciIiU0wmPB0rWq4Iz5he76tWr06pVq8oehoiIiIhUdYVmzMu2xtzfT/4UEREREfn1KzRjrsBcRERERKRyHPcIzMt486cCcxERERGR8jrhLGUxAVDX60PnS6TAXERERESkvApmzOteAdXK9tR3BeYiIiIiIuVVMGNexvXloMBcRERERKR8fjoNOdmu4zLuyAIKzEVEREREyqcC9jAHBeYiIiIiIuVTAXuYgwJzEREREZHy0Yy5iIiIiEgVUGjG/DdlbkaBuYiIiIhIeZwo/8OFQIG5iIiIiEj5FFrKojXmIiIiIiKVo+DhQpcFQY2gMjejwFxEREREpDwKZszLMVsOCsxFRERERMru3LmfA/NybJUICsxFRERERMru9BE4l+c6LsdWiaDAXERERESk7Cro4UKgwFxEREREpOwq6OFCoMBcRERERKTsNGMuIiIiIlIFVNAe5qDAXERERESk7I5XzFM/QYG5iIiIiEjZnXCWsphqUKdJuZpSYC4iIiIiUlYFM+Z1r4CAwHI1pcBcRERERKSsCmbMy3njJygwFxEREREpm59OQ06267icWyWCAnMRERERkbLx3JFFM+YiIiIiIpWkArdKBD8H5saYdGPMV8aYbcaYJCetoTFmrTFmj/N6uZNujDGzjTF7jTGpxpgYj3YSnPJ7jDEJHumxTvt7nbqmuD5ERERERCpMBW6VCL/MjHlPa22UtTbOeT8dWG+tDQXWO+8BbgVCna8JwMvgCrKBGUAXoDMwwyPQftkpW1Cvbwl9iIiIiIiUX34efP3Oz+8/eQ62LoBz+WVusjKWsgwA5jvH84E7PNLfsC6fAQ2MMc2APsBaa+0xa+2PwFqgr5NXz1q72VprgTfOa8tbHyIiIiIi5ZOfB0vGws7lP6dlfg0rE+FfCa78MvB3YG6Bfxtjko0xE5y0K6y1hwGc16ZO+pXAAY+6GU5acekZXtKL60NEREREpHy+XAS7VnnP27UKUheXqVl/B+bXW2tjcC1TmWyM6V5MWeMlzZYh3WfGmAnGmCRjTFJmZmZpqoqIiIjIpSplQfH5W0vIL4JfA3Nr7SHn9QdgGa414t87y1BwXn9wimcALTyqBwOHSkgP9pJOMX2cP75XrLVx1tq4Jk3K9whVEREREblEZB8sIT+j+Pwi+C0wN8bUMcYEFRwDvYHtwEqgYGeVBGCFc7wSuNPZneVaINtZhrIG6G2Mudy56bM3sMbJO2GMudbZjeXO89ry1oeIiIiISPnUv7KE/ODi84sQWKZavrkCWObsYBgI/MNa+74xZgvwT2PMeGA/MNQp/y7wW2AvcBoYB2CtPWaMeRTY4pT7H2vtMef4XuB1oBbwnvMF8GQRfYiIiIiIlE/0GDjwedH5MWPK1KxxbWgicXFxNikpqbKHISIiIiJV3bl8mNcXDnxxYV5YPxg6H6oFeK1qjEn22Ea8EH/OmIuIiIiI/PpUC4DwQT8H5rUaQeNQ10x5x5FFBuUlUWAuIiIiIlJanjd4jl0FV4SXu8nKeMCQiIiIiMjF7cf0n48bXFUhTSowFxEREREprR//43qt3Rhq1K2QJhWYi4iIiIiUhrWQ5QTml1fMbDkoMBcRERERKZ0zP8LZ467jClrGAgrMRURERERKx3N9+eUhFdasAnMRERERkdIoWMYCWsoiIiIiIlJpNGMuIiIiIlIF/OgxY6415iIiIiIilaRgKYupBvWDK6xZBeYiIiIiIqVRsJSlfjAEVK+wZhWYi4iIiIj46lw+ZB1wHVfgMhZQYC4iIiIi4rsTh+Fcruu4AndkAQXmIiIiIiK+89OOLKDAXERERETEd4V2ZAmp0KYVmIuIiIiI+MpPDxcCBeYiIiIiIr7TUhYRERERkSqgYClL9dpQp0mFNq3AXERERETEVwVLWRpcBcZUaNMKzEVEREREfJF7xrVdIlT4+nJQYC4iIiIi4puCBwtBhT9cCBSYi4iIiIj4xo83foICcxERERER3/hxq0RQYC4iIiIi4hvPGXMtZRERERERqSSFlrIoMBcRERERqRwFS1lqN4IaQRXevAJzEREREZGSWPvzw4X8cOMnKDAXERERESlefh58/n9w9rjr/bE02LoAzuVXaDclBubGmCuMMXONMe8579sbY8b72oExJsAYk2KMecd538oY87kxZo8x5i1jzGVOeg3n/V4nP8Sjjf9y0ncbY/p4pPd10vYaY6Z7pHvtQ0RERESkVPLzYMlYeH/az2lnjsHKRPhXgiu/gvgyY/46sAZo7rz/Bri/FH38Htjl8X4W8FdrbSjwI1AQ5I8HfrTWtgX+6pTDGNMeGAGEA32Bl5xgPwB4EbgVaA+MdMoW14eIiIiIiO++XAS7VnnP27UKUhdXWFe+BOaNrbX/BM4BWGvzAJ/m7Y0xwcBtwGvOewPcBCxxiswH7nCOBzjvcfJvdsoPABZba89aa9OAvUBn52uvtfZba+1PwGJgQAl9iIiIiIj4LmVB8flbS8gvBV8C81PGmEaABTDGXAtk+9j+c8BUnKAeaARkOcE9QAZwpXN8JXAA3MF/tlPenX5enaLSi+tDRERERMR32QdLyM+osK58CcwfBFYCbYwxnwBvAL8rqZIx5nbgB2ttsmeyl6K2hLyKSvc2xgnGmCRjTFJmZqa3IiIiIiJyKatfwvxu/eAK6yqwpALW2q3GmB7ANbiC3t3W2lwf2r4e6G+M+S1QE6iHawa9gTEm0JnRDgYOOeUzgBZAhjEmEKgPHPNIL+BZx1v6kWL6OP/cXgFeAYiLi/MavIuIiIjIJSx6DBz4vOj8mDEV1pUvu7LcCYwCYoEYXDdZ3llSPWvtf1lrg621Ibhu3txgrY0HPgCGOMUSgBXO8UrnPU7+BmutddJHOLu2tAJCgS+ALUCoswPLZU4fK506RfUhIiIiIuK7qFEQ2tt7Xlg/6DiywroqccYc6ORxXBO4GdiKa0lLWUwDFhtjHgNSgLlO+lxggTFmL66Z8hEA1todxph/AjuBPGCytTYfwBiTiGvHmADg79baHSX0ISIiIiLiu2oBcO1k2PNv1/sa9aBpe9dMeceRrvwKYlwTzKWoYEx9YIG1tn+FjaIKiIuLs0lJSZU9DBERERGpara8Bqv/4Doe9ga0H1DmpowxydbaOG95ZXny52lcy0lERERERH79Mnf/fNz4Gr91U+JSFmPMKn7e1aQarof5/NNvIxIRERERqUoKAnMTAA1b+60bX9aYP+1xnAf8x1pbcRs2ioiIiIhUZQWBeaM2EHiZ37rxZbvED/3Wu4iIiIhIVZaTDSe/cx03vtqvXRUZmBtjTuD9wTwGsNbaen4blYiIiIhIVZD5zc/HTdr5tasiA3NrbZBfexYRERERqeoyv/75uIn/bvwE39aYA2CMaYprH3MArLX7/TIiEREREZGq4ojnjiz+Xcriy5M/+xtj9gBpwIdAOvCeX0clIiIiIlIVuJeymMoPzIFHgWuBb6y1rXA9+fMTv45KRERERKQqKFjK0qAFXFbbr135EpjnWmuPAtWMMdWstR8AUX4dlYiIiIhIZfvpNGQ5q7f9fOMn+LbGPMsYUxf4GFhojPkB137mIiIiIiK/Xkf34N6k0M/LWMC3GfOPgAbA74H3gX1AP38OSkRERESk0hXaKtG/O7KAb4G5AdYAG4G6wFvO0hYRERERkV8vzx1ZfoGlLCUG5tbav1hrw4HJQHPgQ2PMOr+PTERERESkMnnuYV5FlrIU+AH4DjgKNPXPcEREREREqoiCpSx1r4BaDfzenS/7mN9rjNkIrAcaA/dYayP9PTARERERkUqTnwvH9rmOf4H15eDbrixXAfdba7f5ezAiIiIiIlXCsW/hnLMRYeMqEphba6f/EgMREREREakyMj1v/PxlAvPSrDEXEREREbk0KDAXEREREakCPLdK/IWWsigwFxEREREpkJ8HWxfA16td76sFwDdr4Fy+37tWYC4iIiIiAq6gfMlYWJkIuaddaefyYdXv4F8Jrnw/UmAuIiIiIgLw5SLYtcp73q5VkLrYr90rMBcRERERAUhZUHz+1hLyy0mBuYiIiIgIQPbBEvIz/Nq9AnMRkf/f3p3Hy1GV+R//PPdmJStZICEh7ELCEhIiIMuwCgkIibJokEAE5acj4sy44ui4joOKoijLhBFZZAiLiARBQBZRR5BACBACJAGBkIVAIIRASHLv8/vjnCadTvddQrqrTvf3/Xr161afqq7+3q7qrqerq06JiIgA9BvWzvjhVX16FeYiIiIiIgBjprQ9fmw7498jFeYiIiIiIgB7nwK7fLD8uJHHwejJVX36LlWdu4iIiIhIKpqaYZ8zYN5d4X73frDVyLCnfPTkML6aT1+tGZtZDzP7u5nNNrM5Zvbt2L6DmT1oZvPM7Doz6xbbu8f78+P47YvmdW5sf9rMji5qHx/b5pvZV4vayz6HiIiIiEibljy2fvijV8GZd8CYU6telEN1D2V5Bzjc3UcDewPjzWx/4AfABe6+C/AacGac/kzgNXffuSvfUwAAIABJREFUGbggToeZjQI+BuwOjAcuNrNmM2sGLgImAKOAyXFa2ngOEREREZHKFj26fnjo6Jo+ddUKcw/ejHe7xpsDhwM3xvYrgUlxeGK8Txx/hJlZbJ/u7u+4+3PAfGDfeJvv7s+6+xpgOjAxPqbSc4iIiIiIVLY4FuZbbg89t6zpU1f15M+4Z/tR4GXgLmAB8Lq7F65nuhAo9EszDHgRII5fAQwsbi95TKX2gW08h4iIiIhIeSuXwMrFYXibMTV/+qoW5u7e4u57A8MJe7hHlpss/rUK4zZX+0bM7Cwzm2lmM5ctW1ZuEhERERFpFBscxrJ3zZ++Jt0luvvrwH3A/kB/Myv0BjMcWBSHFwLbAsTx/YDlxe0lj6nU/kobz1Gaa5q7j3P3cYMHD34v/6KIiIiIpG5xUWG+TR0V5mY22Mz6x+GewJHAXOBe4MQ42enA7+LwLfE+cfw97u6x/WOx15YdgF2AvwMPAbvEHli6EU4QvSU+ptJziIiIiIiUl+GJn1DdfsyHAlfG3lOagOvd/VYzexKYbmbfA2YBv4zT/xK42szmE/aUfwzA3eeY2fXAk8A64LPu3gJgZmcDdwDNwOXuPifO6ysVnkNEREREpLwMT/wEsLCDWcaNG+czZ87MOoaIiIiIZGHlEvjxrmF49w/DSVdU5WnM7GF3H1duXE2OMRcRERERybWMT/wEFeYiIiIiIpmf+AkqzEVEREREMj/xE1SYi4iIiIhkfuInqDAXERERkUaX8RU/C6rZXaKIiIiISH61rIPZ18L/Xbi+rbUVWlugqbnmcbTHXEREREQaT8s6uHEq3HI2vPLM+va5v4MbTg/ja0yFuYiIiIg0ntnXwtwZ5cfNnQGPTa9tHlSYi4iIiEgjmnV12+MfaWd8FagwFxEREZHGs+KldsYvrE2OIirMRURERKTx9BvWzvjhtclRRIW5iIiIiDSeMVPaHj+2nfFVoMJcRERERBrP3qfAyOPKjxt5HIyeXNs8qDAXERERkUbU1AxH/ef6+116wLb7w8SL4KQrM+nHXBcYEhEREZHG9OKD64eP/Dbs/+nssqA95iIiIiLSqP7xl/XD2x+YXY5IhbmIiIiINKZCYd6jP2y1e7ZZUGEuIiIiIo1o5RJYviAMb3cgNGVfFmefQERERESk1nJ2GAuoMBcRERGRRrRBYX5QdjmKqDAXERERkcbz/F/D3+79YOs9ss0SqTAXERERkcaycim88kwY3u4DmfRZXo4KcxERERFpLIW95ZCbw1hAhbmIiIiINJriwny7fJz4CSrMRURERKTRFE787N4XhuyVbZYiKsxFREREpDG0rIO/XQzLngr3rQlmXwutLdnmilSYi4iIiEj9a1kHN06FO85d37b6dbjlbLjh9DA+YyrMRURERKT+zb4W5s4oP27uDHhsem3zlKHCXERERETq36yr2x7/SDvja0CFuYiIiIjUvxUvtTN+YW1ytKFqhbmZbWtm95rZXDObY2afj+0DzOwuM5sX/24Z283MLjSz+Wb2mJmNLZrX6XH6eWZ2elH7Pmb2eHzMhWZmbT2HiIiIiDSofsPaGT+8NjnaUM095uuAL7j7SGB/4LNmNgr4KnC3u+8C3B3vA0wAdom3s4BLIBTZwDeB/YB9gW8WFdqXxGkLjxsf2ys9h4iIiIg0ojFT2h4/tp3xNVC1wtzdF7v7I3F4JTAXGAZMBK6Mk10JTIrDE4GrPHgA6G9mQ4Gjgbvcfbm7vwbcBYyP4/q6+9/c3YGrSuZV7jlEREREpBG97+jK40YeB6Mn1y5LBV1q8SRmtj0wBngQ2NrdF0Mo3s1sqzjZMODFooctjG1ttS8s004bz1Ga6yzCHndGjBixif+diIiIiOTe/D+uH+43Arw1HL4ydkooypuas8sWVb0wN7PewG+Af3H3N+Jh4GUnLdPmm9DeYe4+DZgGMG7cuE49VkREREQS8tTv1w9P+S0M2jm7LBVUtVcWM+tKKMqvcfebYvPSeBgK8e/LsX0hsG3Rw4cDi9ppH16mva3nEBEREZFGs3Y1LLgnDA96Xy6LcqhurywG/BKY6+4/KRp1C1DoWeV04HdF7afF3ln2B1bEw1HuAI4ysy3jSZ9HAXfEcSvNbP/4XKeVzKvcc4iIiIhIo3nuT7D2rTC864Rss7ShmoeyHAhMAR43s0dj29eA84DrzexM4AXgpDjuNuAYYD7wFvAJAHdfbmbfBR6K033H3ZfH4c8AVwA9gdvjjTaeQ0REREQazdO3rR/e9djscrTDQocmMm7cOJ85c2bWMURERERkc2pthZ/sBm8uhS0GwRefyfRETzN72N3HlRunK3+KiIiISP1aNCsU5QC7js9F7yuVqDAXERERkfrTsg4euRquO3V9W5ctoLUlu0ztqEk/5iIiIiIiNdOyDm6cCnNnbNj+0DR4czGceAU0568M1h5zEREREakvs6/duCgvmDsDHpte2zwdpMJcREREROrLrKvbHv9IO+MzosJcREREROrLipfaGb+wNjk6SYW5iIiIiNSXfsPaGT+87fEZUWEuIiIiIvVlzJS2x49tZ3xGVJiLiIiISH3Z4wRo7lp+3MjjYPTk2ubpoPz1EyMiIiIi8l7MuwNa1obh3kOgqUs4fGXslFCU5/QiQyrMRURERKS+zLpm/fCpv4Ehe2SXpRN0KIuIiIiI1I83FsGCu8Pw0NHJFOWgwlxERERE6sns6eCtYbi9k0BzRoW5iIiIiNQHd5j16zDc3C2cBJoQHWMuIiIiImlrWQezr4UHLoblC0Lb1ntAj37Z5uok7TEXERERkXS1rIMbp8ItZ8PLT65vX/QI3HB6GJ8IFeYiIiIikq7Z18LcGeXHzZ0Bj02vbZ73QIW5iIiIiKRr1tVtj3+knfE5osJcRERERNK14qV2xi+sTY7NQIW5iIiIiKSr37B2xg+vTY7NQIW5iIiIiKRr71PbHj82nb7MVZiLiIiISLp6Dao8buRxMHpy7bK8R+rHXERERETS5A5//en6+1vtDqtXhMNXxk4JRXlTc3b5OkmFuYiIiIik6YW/wYsPhuHtD4apt2ab5z1SYS4iIiIi6Shc5XPW1bB49vr2A87JLtNmosJcRERERNJQuMpnuQsKPXIl7HQ4NKdb3urkTxERERFJQ1tX+Xzq1qSu8lmOCnMRERERSUMdXeWzHBXmIiIiIpKGOrrKZzlVK8zN7HIze9nMnihqG2Bmd5nZvPh3y9huZnahmc03s8fMbGzRY06P088zs9OL2vcxs8fjYy40M2vrOUREREQkcXV0lc9yqrnH/ApgfEnbV4G73X0X4O54H2ACsEu8nQVcAqHIBr4J7AfsC3yzqNC+JE5beNz4dp5DRERERFK258ltj0/oKp/lVK0wd/f7geUlzROBK+PwlcCkovarPHgA6G9mQ4Gjgbvcfbm7vwbcBYyP4/q6+9/c3YGrSuZV7jlEREREJGUrF1Uel9hVPsupdX8yW7v7YgB3X2xmW8X2YcCLRdMtjG1ttS8s097Wc2zEzM4i7HVnxIgRm/o/iYiIiEi1FPotf+iy9f2WN3WBIXvCm8uSvcpnOXnp6NHKtPkmtHeKu08DpgGMGzeu048XERERkSqq1G9567pQkJ/5x6T7LS9V615ZlsbDUIh/X47tC4Fti6YbDixqp314mfa2nkNEREREUtJWv+VzZyTfb3mpWhfmtwCFnlVOB35X1H5a7J1lf2BFPBzlDuAoM9synvR5FHBHHLfSzPaPvbGcVjKvcs8hIiIiIimp837LS1Vt37+ZXQscCgwys4WE3lXOA643szOBF4CT4uS3AccA84G3gE8AuPtyM/su8FCc7jvuXjih9DOEnl96ArfHG208h4iIiIikpM77LS9VtcLc3SudFntEmWkd+GyF+VwOXF6mfSawR5n2V8s9h4iIiIgkpkv3tscn3m95qfo5Wl5ERERE0lbogWXW1fDa8/Dm0ranT7zf8lIqzEVEREQke5V6YKmkDvotL6XCXERERESy11YPLAADdoR1a+qq3/JSKsxFREREJHvt9cDSays4847aZMlIrbtLFBERERHZWIP1wFKOCnMRERERyV6vQW2Pr7MeWMrRoSwiIiIiUnsb9MDyArz1StvT11kPLOWoMBcRERGR2lIPLGWpMBcRERGR2lIPLGWpMBcRERGR2lIPLGWpMBcRERGR6io+nnzFS+1f0bMBemApR4W5iIiIiFRPZ48nh4bogaUcdZcoIiIiItXT3vHk5TRADyzlaI+5iIiIiGxexYeuLJrVucc2SA8s5agwFxEREZHNp7OHrjR3Cyd7NlgPLOWoMBcRERGRzaezh65sM7Yhe2ApR4W5iIiIiGy60h5XVr/eucc36PHk5agwFxEREZFNsyk9rhRr4OPJy1FhLiIiIiIdU7p3vLkrvPZc5+bR3C0cvtLgx5OXo8JcRERERNr3XveOF3zoAhhz6maJlHfrWtcxY8EMbpp3E0veWsKQLYYw8KiBAytNr8JcRERERDa2OfaOl6qjQ1dKi+6te27NDv124Lk3nmPpW0vZuufWrG1dy5PLn3z3MUtWLWHoKUO3rzRPFeYiIiIisqHNtXe8Wx/o0S+prhDL7eWeuPNEDOPm+Te/W4SXK7pnvzJ7g/vluHvF51ZhLiIiIiIb7iFf9gysfu29z3PCebk6bGVT93I/uuzRDeZTqejuCDOrOE6FuYiIiEijKT1Mpe820PIOLJ7d/mM7qkqHrXRkj/bm3stdKyrMRUREROpZR4rwNxa+9+cZsCOsW9Phw1Y2pcBevGoxq9etZsWaFe/Op9Ie7c25l7tWVJiLiIiIpKpc0T1oF3hlHryxaLMX4euAGb17cVOf3izp0syQdS1MXPkmNnQ0N285kCVvLWXIFlsxsVcPbP7NG+yt3tRDRkrbUtfWMebW1shGMm7cOJ85c2bWMUREREQ2Lrj7DYPRp4AZPHpNxT3fFQtn4OZOtm29roUd1q7lua5dWRrvrzV4snv3qv3bTa3OIY87h81uZdBKeKUP3Len4QaHPeahrTcsHmhss9wZWGmajrZlMK97Rzdx4U/mlD3QXIV5pMJcREREOmrd2tXM+NM3uGnh3SxpXcOQpm5M3PpgtnxoMS1/mkOvN1pZ1beJpoNGYk1Gy/1PVm7rA2sG96TbstX0WulhmlFdsbdfo2VBT3q9aazq7TTt9DYGbbYt7wMvplq09oZuLbDj0qyXbvWNfGquCvO27DRyL9/7nEtZ/PrbDO3fkxPHDscMbnh4YcW2If16sNPg3ixYtoolKzr+OM1L89K8NC/NS/PSvNKa18l7DWTQPf9By31P0OuN1o0L4AYqKqvNgcr9lnR8ms35fJtzXu7OqKefaqzC3MzGAz8DmoH/cffz2pq++9BdfOjpP61JNhEREUlH19a3+cGcbzFyQUu7025qkZdyoZnFvFJXaY95U62D1IKZNQMXAROAUcBkMxuVbSoRERFJ0SdfncbIBS2U7sost2tzUwvPzVmwNsK86lVdFubAvsB8d3/W3dcA04GJGWcSERGRBI1+NvRiUlpYqtCUza1eC/NhwItF9xfGtg2Y2VlmNtPMZra8taJ0tIiIiAh93qzPw35TlvISaesw8notzMt9id3oVXD3ae4+zt3HNW/RrwaxREREJDUre1d/3/jmLDQbYV4p/1phVjl9vV5gaCGwbdH94cCijLKIiIhIwmbvOJztFr+40cmL7d3vjLwey53FvHqMGoV1787aJUvoOnQo/SZNgiZjxU2/Ze3ixXQZMoTuO+zAmueeqzhNR9uymNc3br/9H1dXeo3qsVcWM+sCPAMcAbwEPASc4u5zKj1GvbKIiIhIOV18DT984hsd6pXl7W378krrm6F/8n5NNB08CsxovX8OvVZUaOsDa7aK/Zi/4XGa3WHATrTe9kd6LX+bVQN60nTsEYDR+vs22rbsQZ+dd2PIqy2sW7I0uaK1/wkn0G/SRKy5uerLNStm9rC7jys7rh4LcwAzOwb4KaG7xMvd/T/bmn6nUXv5mM9dyqLX32ab/j05YZ/hNBncMHNhxbah/Xqw41a9eXbZKhZ34nGal+aleWlempfmpXmlNa+TRg9k0N3fpOVPj4diuq/RZ8Rwhrw9kHVLlzZMUSnvXUMW5p2lK3+KiIiISLW1VZjX68mfIiIiIiJJUWEuIiIiIpIDKsxFRERERHJAhbmIiIiISA6oMBcRERERyQEV5iIiIiIiOaDCXEREREQkB1SYi4iIiIjkgApzEREREZEcUGEuIiIiIpIDKsxFRERERHJAhbmIiIiISA6Yu2edIRfMbBnwPDAIeKVkdEfaNvVxmpfmpXlpXpqX5qV5aV6aV+PMazt3H0w57q5b0Q2YuSltm/o4zUvz0rw0L81L89K8NC/NqzHnVXrToSwiIiIiIjmgwlxEREREJAdUmG9s2ia2berjNC/NS/PSvDQvzUvz0rw0r8ac1wZ08qeIiIiISA5oj7mIiIiISA6oMBcRERERyYGGLczNzLLOUA9SfR1Typ1K1lRyyuaVynJPJWdqUnpdU8pakFLmlLLmWUMV5mbW38y6ALi7ayXaLHoV3zGz3K5TZratme0E+V/+Znacmf0KQtas81RiZoeY2dmQ75wAZnaAmX046xwdYWajzGyHrHN0UM/iOzl+X22RdYA61b34To6XPySyvdK2qjpS+VzN5UpZDWZ2NHALcImZXQD5XInM7J/M7KC8fmAUi6/pjWb2dTP7DwB3b81jdjM7BrgduMjMbof8fuCZ2QeBHwJ7mdmRWeepJC7/a4HDzKxXe9NnycwmAv8DvFnSnsflP4HwunbNOkt7zOwoYLqZfdvMJkNuP1c/CFxvZueb2b9mnactiW0DxhOW/7lmNhXe/VzNXfZUtlfaVlVHSp+ruVohqyWuMD8DfgRcCgw3s1OyTbUxMzsEuI+Qc7+8fWAUM7N9gQvj7TfAQWZ2PeTvw87MxgDnAZ9y9/HA62bWH/JXRMRC53zgX4HrgQOyTVRe/JD7NvBFoD9wUraJKjOzfsAZwCfd/S4z62Fm3SCXy/8I4BfAp939GTPrWjI+T++rwrp6DdACjC0Zn4tCIhZkPwd+BcwkFBEHZZuqvMS2AeMIXb/9mnCJ8fFm9j/w7jYgF8sf0tleaVtVHSl9rkKdF+YWbAEcDHzF3WcAj8TbNpmGK2Fm3YGdgROAq4H/AD6QtxWmyBbA9e5+m7vPJWz0DjOz6RA+7DJNt6FW4B53/5uZDQcOB35kZjfH9SPzIiKuqwOAk4Gz3f0PwD3AZ83ssCyzFYs5twH+E/iqu/8voeiZYmY7ZpuuotWAA6/G5X8zcJWZ3WJmPSH75R8z9AImAg8CD5vZQOD8uDf6m5CPgieuAwOBTwL/6u7XEYrJfc3s42Y2CfJRSJhZH8Jn6pfc/UZgBuHzYNdMg5URvyymtA1oBX4bX9crgc8Bg81sGuRj+RdJZXulbdVmFLMm8blaLK9v+M3Cg7eAKwgLpCl+WMwF9s00XAl3fwe4CbjL3S8G7gb+HTjQzJozDVeeAZ80sz3j/Z0Je0+7mtlHs4sVFN5kcaPWCuxqZj8D7gd+AvwLsBb4LWS/EYnr6nLg8+7+ZzPr6u4PEX4mPMjMuuRhAx1zLgImuPt9Fs7ZmAk8D2wPkIf1tWj5N8f31sPAGMLenVuATwGF91zmyz9mWEX4rJpP2BP1d8KeyGeAfczs/DhdHtbVV4HPufvdZrYV4T01C9gaONHM/i3LjAXuvhL4Kes//1cBDwB7ZJtsY+6+hvDF4c5EtgEOHG5mu7n7GndfBpwF9DGzj2WcrVRut1faVlVPzLoK+CWwAPgxOf1cLdYl6wDVEn9m2x14HJgXP6AL3gEGxemmAAPc/We1TxlOSAP2AZ4AHnX3NwHc/fz4hj0XOMfMDga2dPefZJEzZt0f2At40N3vNbOvATeY2cOEXyAOB/rFW9aGAIvjnpDHzezrsX0gcHF8s55kZreZ2eC4UclEybr6PLDK3dfG0U8DXwUudfdlZmZZfYCY2WhgJ+CvwCoAd18HLDSzZ4ELzGxcUfYsDQRecfeWeP9xwh7IfwC/iJ8HJ8W95gNjoZmJWCxsC8x290fMbA3wWeDHsUDDzF4EMj9xNa4DOwL/BxQ+U98Gvubud8YC8jTil7SsmNkgd38FwN2fLBm9FhgepzsJ6Obu19Q44ruKlz+wqPD+zuk2YB9gF8K2apaFk/5uMrOJ7j4PeI2wZ3JoVhkLEtpeaVtVBXH5jyZ8EZ8HXEb4ovsTd78oTpOLz9VSufhWs7mZ2bHAVcChwNeJL3zRXodnCW+AY4FzgLsyiFk4ceYyYAfCCv2B4pzu/iPCsVt/BL4F3JlFzpjpWGA6Ya/jl+IH8p3AfoTsH4xvwt6EY/gtq5+GLJzo95KZfbGoeRYwh3Dy39g43cnAVsCamoeMStbVfweOje1NAO5+K/Ak8PO49zerovx4wt68kwjr7BfM7N3DAdz9e4TXOA+/lhwPzDazMwpt7n4zYY/OKOAAM9smFmVDgXXZJH13+d8ATAWuMbND3P0J4HuE17lgT2CEmXXP8H1VWAdOjtm+ZGaj3H2lu98JEL8I9Y9Zu2SRtdzyjx9Hhe3d88DTFk5c+wphD1omSpb/r4F/iu1dIXfbgPGEQ2yOJJzwuVf8kvBr4FYz2z3u9TdgXFbLP2ZNYnulbVV1FC3/vQm/jFxEOJzpM4ROAAoy/1wty93r6kb4ifIJ4P3x/hTgz0D3omm2J/xk9Ciwe0Y59yZ86zww3v8u8CXCm29A0XQfBlYAozJ+Xb8FnBqHdwU+D9wKjCua5gzgRWC3DHMOB24jbHAXEo4tLR5/BvAYYQMzC9gjw6xtrqtAU/x7EOGkpT4Z5TTC4QAT4v3DCF94fwG8r5AV+AHhp8LmDF/TnQjnkHyXsOfujJLxHyH8PPxLwk/Fe2aYdQzhsLr94v0vA38pM93UuK5m9hnQiXXgkzHryLwt/6L302jCXvMHM37/l1v+fy6Td1LW2wDCoZ8LWL+9uhg4Hdgi3j+LcLjY5YQdX5lsV4vyfoucb6/QtqqWy/+cuPzHFk2T+edqxfxZB6jCAukXV5qmorZbgV2L7g8H/lLYoGSUs2vhQ4Kw1+4lwjFkv4or9vZxmqlZf8jFjN8HLiu6Pwg4G/hvwk9ug4GvkdEGuShXE3B0HB4FLCvzgTcqbhRHZJy13XU1tm0BDMo466+AC4ru70UozM4FesS2AcB2GedsBibG4SMIX75Li/OeQF9gcMZZdwQml7TdSjhcoXB/B+DGPGw82lkHusX31N+z/Lzq4PLfg7DXPLPP/04s/+7AJ7LeBgB9gP3j8HDgVULvJr8B/i227xZvmX6uxiy5315pW5Xp8t+R8EtV5p+rZfNnHWAzLwyLf7vGv83x733AB+Lw7vFv7wxzNpXcPwb4TBweTfi56JDi/yHrG+Hn6fuBLxa17UH4uaiwx6xbHpZ/ybowsvgDj/BT5tAcvJ4dWlfz8CEXs2xPKBBPLWo7CvgDRb/wZJyxqUzbYYTi7Mx4fxQwMOushbzEPUtxuHAi7a6xbbv4d4uss3Z0HQD65X35x799c/B6trv8Y9tG/1eWryvhcLUvx+FjgN+T8Q6ZMplzvb3StioXy79X1jkr3erqGHOPr7avPxmhcEz5y8BiC1f9Oz+eGPRmuXnUgpd0zeShC6dL4vBsQp/A28b7LRvPobYs9GbwOuGn1gPN7MsAHo6FbQHeHyfN9KS/wvIvDMezxecSjtv8vIWLNfwis4BFOriu/jCLbBUsIexhODSeMI2H44rfpqT/6qyUvq9i273AvwFnmtmNhOsY5IK7t/r6k9K7EHq5eANYEo8p/amZ9fXQs1QetLUOFD4D3sgoW0eX/7R4sm9mOQs6sPwvIHwpy7QrvzLbq+vc/Ydx+DbCScDbZZGtnBS2V9pWVU8Hln+hR768fK5uJOleWSqd9Vto93AiCsALhIP/BxEuMvJKnnKWtJ1MKHS+U6t87Sl8MLv7A2b2feAHZjaS0N3Q/oSfsyn3P2ahaPmvBXD3uWZ2OfDPwGHuvjjbhOvlbV0tVZRvtZn9nnBuxkfjGe9PEM6VeCrLjKVK31fufo+ZzQQ+RjjpK7MeWAoKGUuXv5k9Tbi4zF7AWVkXkPEkrhaAdtaBOXGaTD4DinPmffmXy5rC8q+wvfoo4VeoOVnkixmair885HV7VZoztuVyW9VO1lxtq8xsMLCikCuvy78zUt9j3h827HA/rlBuZnvFb3IQjok7BPi4uz+ew5wT4xnspxIK8lPc/bkMcmJmo83seDPbxdZf0KA5/t0D2BI4nlCMrQMm5S1r8etamI7Q483hGS3/jmTNxbpqZvua2Slm9n4z2yLmK+whGUzo//Uc1nc5N8ndF9Y6Z7mssa3wvtrNzA6MbfsRTko6wsMvUplnjRmLsx4cJ90dOBGY4u6PZZT1MDP7HoRf7MysOY/rQIWceV3+7WXN+/IvznqAhavnnkq4+u9kd38xo6zHA+dZUb/ZheE8ba8q5czptqq9rLnYVsVckwhfDkYU6qs8Lv9O8xwcT7MpN0JvJauBE+L94mO2DiH0ePJP8f5OZHSiTwdzFs50HwZsn+Fr+iHCHrDfEI7F+kjRuENj1kOzXvYdyFr6ujaR4fFvHcyah3V1Qsw5jXBiz8FF4w6L4/bLetl3Imvh5OouZHisZmdeV0LfyrtmlNMIJ3HeTOin/kcl43OxDnQg56F5Wf6dyJrC8i9k3SfeHwPsmOF6cDShB54jy4w7PC/bq3ZyHpazbVVHsma+rYrPvy+h4D6kzLgj8rL8N+l/yzrAJi6QHYF7Cd2evQqcGNsLXfb8M7H4IcMTZzqZ07LIWJR1LKEP0jHx/jnA74rGp5o105NnO5k1y3V137jRLfS8cCGhu67ehB4ZPlO0/ma9/DuTNevl3+GsebmU0gaDAAALA0lEQVQRupQ8G7gO+O/Y1gT8P8rsYMh5zrycPN9u1rzcUshKONTn+aLPzgGEY8d3IvS69PE8fF51MmfWn1WdyZrpCckxw8mEiwUBjCBc2Oxkwg7OjwAnZ738N/VWONs2KWbWh/Cz5M1mNoHwAXKGu99YMt27x8dlIZWcMcMIYLy7TytquwP4lLu/UNS20bFntVanWbNeVwcT9n781cy2JvRJ/CjhhL9lhG7yXi4cZ5hVTmWtatbCce8fJfS28i3CCWhOOGzl4+6+JuusqeQEZa0WM9uKcGGjWwhXof054f3USugb/r/c/c2ss6aSE9LKCmBmxxDqqy+Y2Z8J5zm0Eq6kfqK7v5iXrJ2W9TeDztwIfWluGYebi9rHE85mL3yb24cMu8NKJWeZrIWLBXQDegB/I3aDRbgMc6bdtilr1XIOKGn7BPC5OHwg8L/En69z8Joqa3Wy9i263w34YRw+gXBYw++VU1mzvhW/rwjX/5gB/IOwo6PwvrqG+AulctZl1r5xeDDwNOHwwH8umuY84NNZZ30vt2RO/jSzEwlddd1uZmcSzgQHwN3/QPgJ41Izu5LQzVQP5WxbSdYzgPfFUeuAd4ClwCILly7/PuFiF5lQ1qrmvM3MzjCzPQHc/Vfu/vM4/FfCz9hDsshYoKzVUZT1zqKsLcBAM/sOYf08B9jCzM5XzvYpa3WUvK/Ois2nEvpUvwzefV91JcP3VSo5IdmsdxZlnUC4sufhRZO2EC4gl6wkDmUxs2HA7YRLAPcjXKL4LeAP7n5/0XSXEs5oP9wzOKM9lZztZL3d3f9clLMvYa/uGZ7dmdfKWrucd7r7fUXTnQB8g3A1xedrnTNmUNYqqJB1NXAT4fyY7xIudnJznLbZiw6/Uk5lzTDrhwk7OW5w94eKpiu8r47Pybqay5yJZ+1LOIZ8NaEzhXeAewiHXbUQaquPu3uuuvHtjFT6Me9B+DntMQ/dNy0i7Hk+ysxedvenzOz9hCs7ZVbsJpSzraxHm9lyd58D7Ew4834/d5+vrHWVtVLOI81sCbCA8CH4BcKhV5kUj5GyVkelrBMIV6D8kLs/HY/TfEk5O0RZq6NS1olmtjJuW6cC5xJOXsykgEwoZ+pZF8esJwP/RTgs+GjCCaunplyUQyL9mLv7AsIJU18xsx7u/gzhJ42BrL+K01OEM8YzK3ZTyQntZi1cGe3rhK7dsix0lbUK2svp4YIXDwDHxS8TmVHW6qiQ9UbCT9ZbFhVlmf6smkpOUNZq6eC29X7g2CzfV6nkhLrJ2p+wg+Mld7/c3c/3cAXVpOW+MDd796I8NxOuMHWKmfV096cJP7mdZmZ93H2luy9VzvZ1IOupZtbb3R9w9yczC4qyZpTz9Jjziay/6ChrdbSR9SlC1qkWL4SUWUjSyQnKWi2d2LY+m+X7KpWcUHdZTzGzXpkFrILcFuZFC6PgXmAW4WTKc82sK+GqTm8RunLKRCo5odNZ19U43gaUdfNLJScoa7V0MmtmXY2mkhOUtVpS2bamkhPqOmum3U1vbrk7xtzMBgKr3X0VgLu7mXXz0H/qXYTLQX8IuBvoRejSZ7VyKquypp1TWZU1lZzKqqyp5FTWBHkO+mws3Ahn2t4J3Ad8iqJLPwNHAr8Etor3hwP9lVNZlTX9nMqqrKnkVFZlTSWnsqZ5y013iWa2DeGnismE44jGES6zegPhgiz3Aj/ykqtm1loqOUFZqyWVrKnkBGWtllSyppITlLVaUsmaSk5Q1lTl6VCWLsAL7v4IgJnNJ3R/cwLhksAnuPtCs8zPEk8lJyhrtaSSNZWcoKzVkkrWVHKCslZLKllTyQnKmqTcnPzpoY/MNyxeXczdnyX8pLEU2DYukKasF0gqOUFZqyWVrKnkBGWtllSyppITlLVaUsmaSk5Q1lRlWpib2YFm9hEzOzU2fQPoY2ZfhHf7rpwJTLbQd2UmZ4mnklNZlTWVnMqqrKnkVFZlTSWnstaHzApzMzsGmEa4CubXzOz7Hvp2/i2wk5n9LE7am9BtT7Nytk1ZqyOVrKnkBGWtllSyppITlLVaUsmaSk5Q1rrh2Zx5uwvhW9BB8f72wAygL9AT2B24DrgNeBwYo5zKqqzp51RWZU0lp7Iqayo5lbW+blkulJPjcDPhErAPAHuUTDcE6JfxypP7nMqqrKnkVFZlTSWnsiprKjmVtb5uNT2UxcxGWLha0z/c/frY3OrurwLzgVVxun0A3H2Ju6+oZcaUciqrsqaSU1mVNZWcyqqsqeRU1vpUs8LczI4l/CxxMfBrM9stjip02TgA6BVPAphuZoNrla1YKjlBWasllayp5ARlrZZUsqaSE5S1WlLJmkpOUNa6Ve1d8oAB2xKOEzoU2Br4ArAI2L1ousuBG4G/FLfX6pZKTmVV1lRyKquyppJTWZU1lZzKWv+3Wi2YZsLZt8Pg3auNngO8BOwa7/+Y8FPGbpm9GInkVFZlTSWnsiprKjmVVVlTyams9X2r9sLYGXg/4cD+64Avl4z/MnBlXGgTgB0zWmmSyKmsyppKTmVV1lRyKquyppJTWRvjVs0F8iHgMeBPwC+A44F/AOcWTbM9cFmmL0AiOZVVWVPJqazKmkpOZVXWVHIqa+PcCgfdb1ZmdgBwPjDZ3WeZ2TRgX+AA4AEzawamAwcBY8xsgLsvr0aWesiprMqaSk5lVdZUciqrsqaSU1kbTJW+KR0ATC26Pxj4fRzekXCQ/8WEDub3zOpbSSo5lVVZU8mprMqaSk5lVdZUciprY92qtVCagb5Fw8OBWcDQ2LYdoYucrDu5TyKnsiprKjmVVVlTyamsyppKTmVtrFtV+jF39xZ3fyPeNeB1YLm7L459VH4N6OoZdxyfSk5Q1mpJJWsqOUFZqyWVrKnkBGWtllSyppITlLWRFLqtqf4TmV0BLAaOIvzE8XhNnriTUskJylotqWRNJScoa7WkkjWVnKCs1ZJK1lRygrLWq6oX5mZmQFdgbvx7hLvPq+qTboJUcoKyVksqWVPJCcpaLalkTSUnKGu1pJI1lZygrPWulnvMpwIPufucmjzhJkolJyhrtaSSNZWcoKzVkkrWVHKCslZLKllTyQnKWq9qWZib1+rJ3oNUcoKyVksqWVPJCcpaLalkTSUnKGu1pJI1lZygrPWqZoW5iIiIiIhUVpVeWUREREREpHNUmIuIiIiI5IAKcxERERGRHFBhLiIibTKzb5nZF9sYP8nMRtUyk4hIPVJhLiIi79UkQIW5iMh7pF5ZRERkI2b278BpwIvAMuBhYAVwFtANmA9MAfYGbo3jVgAnxFlcBAwG3gI+5e5P1TK/iEiKVJiLiMgGzGwf4ApgP6AL8AhwKfArd381TvM9YKm7/zxebvtWd78xjrsb+LS7zzOz/YD/cvfDa/+fiIikpUvWAUREJHcOBn7r7m8BmNktsX2PWJD3B3oDd5Q+0Mx6AwcAN4SrcQPQveqJRUTqgApzEREpp9zPqVcAk9x9drzE9qFlpmkCXnf3vasXTUSkPunkTxERKXU/8GEz62lmfYDjYnsfYLGZdQU+XjT9yjgOd38DeM7MToJwKW4zG1276CIi6dIx5iIispGikz+fBxYCTwKrgC/HtseBPu4+1cwOBC4D3gFOBFqBS4ChQFdgurt/p+b/hIhIYlSYi4iIiIjkgA5lERERERHJARXmIiIiIiI5oMJcRERERCQHVJiLiIiIiOSACnMRERERkRxQYS4iIiIikgMqzEVEREREckCFuYiIiIhIDvx/oZpeLWkie4UAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -380,7 +381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,